Apparatus for reading a license plate
Gonzalez et al. - - 4817166
System for monitoring parked vehicles
Bashan et al. - - 5339000
System for detecting and warning an illegally parked vehicle
Morimoto - - 5343237
Vehicle speeding detection and identification
Gerber - - 5381155
Mobile type of automatic identification system for a car plate
Hwang et al. - - 5425108
| DE4401993 | ||||
| EP0367725 | Method and device for increasing anti-theft security in parking lots. | |||
| FR2226904 | ||||
| GB193320 | ||||
| GB2265243 | ||||
| GB2279478 | ||||
| GB2284290 | ||||
| JP08242442 | ||||
| WO/1994/008820 | THEFT-DETECTION DEVICE | |||
| WO/1997/014116 | A TERMINAL FOR USE BY TRAFFIC WARDENS WHEN RECORDING INFRINGEMENTS OF PARKING REGULATIONS |
Gavin, Geoffrey K.
Kilpatrick Stockton LLP
1. A parking regulation enforcement system for monitoring a parked vehicle, the system comprising: a camera capturing a first image of the parked vehicle at a first observation time and a second image of the vehicle at a second observation time; a data processing sub-system coupled to the camera, the data processing sub-system extracting an identifier of the parked vehicle from the first image and the second image; means within the data processing sub-system, for determining a first position of a predetermined measurement point at the first observation time and a second position of the measurement point at the second observation time, the means comprising a global positioning system; a memory within the data processing sub-system, the memory storing a first data record, the first data record comprising the identifier, the first observation time, and the first position of the measurement point; and means, within the data processing sub-system, for comparing the first data record to a second data record, the second data record including the identifier, the second observation time, and the second position of the measurement point, and for generating an output.
2. The parking regulation enforcement system, as recited in claim 1, further comprising: a proximity sensor coupled to the data processing sub-system, the sensor measuring a distance between the parked vehicle and the measurement point; means for triggering the camera to capture the first and second images when the measured distance is equal to a predetermined value.
3. The parking regulation enforcement system, as recited in claim 1, further comprising: a metal detector coupled to the camera; means for triggering the camera to capture the first and second images when the metal detector detects a pre-determined amount of metal.
4. The parking regulation enforcement system, as recited in claim 1, wherein the identifier is a license plate number of the parked vehicle.
5. The parking regulation enforcement system, as recited in claim 4, wherein the sub-system uses a pattern-matching license plate recognition algorithm to extract the license plate number from the first image and the second image.
6. The parking regulation enforcement system, a recited in a claim 4, wherein the sub-system uses a full recognition mode license plate recognition algorithm to extract the license plate number from the first image and the second image.
7. The parking regulation enforcement system, as recited in claim 4, wherein the sub-system uses a pattern matching license plate recognition algorithm and a full recognition mode license plate algorithm to extract the license plate number from the first image and the second image.
8. The parking regulation enforcement system, as recited to claim 1, further comprising a radio frequency transponder coupled to the parked vehicle, the transponder broadcasting the identifier when interrogated.
9. The parking regulation enforcement system, as recited in claim 1, further comprising a display coupled to the camera, the display showing the first image and the second image.
10. The parking regulation enforcement system, as recited in claim 1, wherein the camera is mounted on a second vehicle.
11. The parking regulation enforcement system, as recited in claim 10, wherein the measurement point is a location on the second vehicle.
12. A system for locating a lost vehicle comprising: a sensor capturing an image of the lost vehicle at an observation time; a sub-system, within a data processing system coupled to the sensor, the sub-system extracting an identifier of the lost vehicle from the image; a positioning system, coupled to the data processing system, for determining a position of a predetermined measurement point at the observation of a predetermined measurement point at the observation time; a concatenation system for concatenating the identifier, the observation time, and the position of the measurement point into a vehicle data record; a display device coupled to the data processing system for displaying a location of the lost vehicle on the display device; a database coupled to the data processing system, the database storing a plurality of data records, each data record including a vehicle identifier, the observation time, and a vehicle position; and a search engine coupled to the database for enabling a user to traverse the database to locate a second data record having the identifier of the lost vehicle, to assist in identifying the lost vehicle.
13. A parking regulation enforcement system for monitoring a parked vehicle, the system comprising: a camera capturing an image of a vehicle at a first observation time; a data processing sub-system coupled to the camera, the data processing sub-system extracting an identifier of the vehicle from the image; means, within the data processing sub-system, for determining a position of a predetermined measurement point at the first observation time; means for concatenating the identifier, the first observation time, and the position of the measurement point into a vehicle data record; a database coupled to the data processing sub-system, the database storing a plurality of data records, each data record including a vehicle identifier, an observation time and a vehicle position; means for traversing the database to locate a second data record having the identifier of the vehicle; and a computerized billing system for receiving data, and for creating an output.
14. The parking regulation enforcement system, as recited in claim 13, further comprising: first means for comparing the observation time in the first data record to the observation time of the second data record; and second means for comparing the position in the first data record to the position in the second data record.
15. The parking regulation enforcement system, as recited in claim 14, further comprising third means for comparing a difference between the observation time in the first data record and the observation time of the second data record to a predetermined parking time limit.
16. The parking regulation enforcement system, as recited in claim 15, further comprising the computerized billing system, the billing system receiving data from the first and second comparing means.
17. The parking regulation enforcement system, as recited in claim 16, wherein the computerized billing system measures an overall parking time of the vehicle over a predetermined period of time.
18. The parking regulation enforcement system, as recited in claim 16, wherein the computerized billing system initializing a virtual parking meter in the database, the virtual parking meter comparing a plurality of data records for a particular vehicle.
19. The parking regulation enforcement system, as recited in claim 16, wherein the computerized billing system charges to fee to an owner of the vehicle based upon a fee structure.
20. The parking regulation enforcement system, as recited in claim 19, wherein the fee structure is based on a changing hourly rate.
21. The parking regulation enforcement system, as recited in claim 13, further comprising: a parking facility system for determining a parking fee within a parking facility, the system determining a parking fee by comparing the observation time in the first data record to the observation time in the second data record.
22. The parking regulation enforcement system, as recited in claim 21, further comprising an automated gate coupled to the parking facility system that blocks movement of the vehicle based upon data received from the parking facility system.
23. The parking regulation enforcement system, as recited in claim 13, further comprising a second database storing geographically referenced information.
24. The parking regulation enforcement system, as recited in claim 23, further comprising: a navigation system that records and updates a plurality of citations for vehicles based on the plurality of stored data records; means for estimating a time for patrolling a route based on the plurality of citations and geographic information to estimate the time.
25. The parking regulation enforcement system, as recited in claim 24, further comprising a display displaying information stored in the second database.
26. The parking regulation enforcement system, as recited in claim 13, further comprising a clock within the data processing sub-system.
27. The parking regulation enforcement system, as recited in claim 13, wherein the identifier is a license plate number of the vehicle.
28. The parking regulation enforcement system, as recited in claim 27, wherein the sub-system uses a license plate recognition algorithm to extract the license plate number from the image.
29. The parking regulation enforcement system, as recited in claim 28, further comprising means for comparing a result of the license plate recognition algorithm to a predetermined threshold.
30. The parking regulation enforcement system, as recited in claim 28, wherein the vehicle data record further includes the raster image.
31. The parking regulation enforcement system, as recited in claim 13, further comprising means for estimating an error in the position of the vehicle, the estimated error being stored in the vehicle data record.
32. The parking regulation enforcement system, as recited in claim 13, further comprising: a second database storing a plurality of vehicle identifiers; means for traversing the second database to locate one of the vehicle identifiers matching the identifier in the vehicle data record; means for notifying a user of a located match between the vehicle identifier and the identifier in the vehicle data record.
33. The parking regulation enforcement system, as recited in claim 13, further comprising means for encrypting the vehicle data record.
34. The parking regulation enforcement system, as recited in claim 13, wherein the camera captures a plurality of successive images of the vehicle.
35. The parking regulation enforcement system, as recited in claim 13, further comprising a second camera capturing an image of a second vehicle.
36. A system for locating a lost vehicle comprising: a camera capturing an image of the lost vehicle at an observation time; a sub-system, within a data processing system coupled to the camera, the sub-system extracting an identifier of the lost vehicle from the image; means, coupled to the data processing system, for determining a position of a predetermined measurement point at the observation time; means for concatenating the identifier, the observation time, and the position of the measurement point into a vehicle data record; a database coupled to the data processing system, the database storing a plurality of data records, each data record including a vehicle identifier, the observation time, and a vehicle position; and means for enabling a user to traverse the database to locate a second data record having the identifier of the lost vehicle, to assist in identifying the lost vehicle.
37. The system for locating a lost vehicle as recited in claim 36, further comprising: a display device coupled to the data processing system; and means for plotting a location of the lost vehicle on the display device.
38. The system for locating a lost vehicle, as recited in claim 36, wherein the enabling means is mobile.
39. A method for enforcing parking regulations comprising: capturing an image of a parked vehicle at a first observation time; extracting an identifier of the parked vehicle from the image; determining a position of a predetermined measurement point at the first observation time; concatenating the identifier, the first observation time, and the position of the measurement point into a vehicle data record; storing a plurality of data records, each data record including a vehicle identifier, an observation time, and a vehicle position; traversing the database to locate a second data record having the identifier of the parked vehicle; and billing a customer based upon a billing criteria and the stored data records.
40. The method for enforcing parking regulations, as recited in claim 39, further comprising the steps of: comparing the observation time in the first data record to the observation time of the second data record; and comparing the position in the first data record to the position and the second data record.
41. A parking regulation enforcement system for monitoring a parked vehicle, the system comprising: a sensor for capturing a first image of the parked vehicle at a first observation time and a second image of the vehicle at a second observation time; a data processing sub-system coupled to the sensor, the data processing sub-system extracting an identifier of the parked vehicle from the first image and the second image; a global positioning system within the data processing sub-system, for determining a first position of a predetermined measurement point at the first observation time and a second position of the measurement point at the second observation time; a memory within the data processing sub-system, the memory storing a first data record, the first data record comprising the identifier, the first observation time, and the first position of the measurement point; and a comparator within the data processing sub-system, for comparing the first data record to a second data record, the second data record including the identifier, the second observation time, and the second position of the measurement point, and for generating an output.
42. A parking regulation enforcement system for monitoring a parked vehicle, the system comprising: a sensor for capturing an image of a vehicle at a first observation time; a data processing sub-system coupled to the sensor, the data processing sub-system extracting the identifier of the vehicle from the image; a positioning sub-system within the data processing sub-system, for determining a position of a predetermined measurement point at the first observation time; a concentration system for concatenating the identifier, the first observation time, and the position of the measurement point into a vehicle data record; a database coupled to the data processing sub-system, the database storing a plurality of data records, each data record including a vehicle identifier, an observation time and a vehicle position; a search engine coupled to the database for traversing the database to locate a second data record having the identifier of the vehicle; and a computerized billing system for receiving data, and for creating an output.
43. A system for locating a lost vehicle comprising:
44. The system for locating a lost vehicle as recited in claim 43, wherein the sensor captures the image of the lost vehicle at an observation time.
45. The system for locating a lost vehicle as recited in claim 44, further comprising a positioning system, coupled to the data processing system, for determining a position of a predetermined measurement point at the observation time.
46. The system for locating a lost vehicle as recited in claim 45, further comprising a concatenation system for concatenating the identifier, the observation time, and the position of the measurement point into a vehicle data record.
47. The system for locating a lost vehicle as recited in claim 46, wherein each data record further includes an observation time and a vehicle position.
48. The system for locating a lost vehicle as recited in claim 43, further comprising a display device coupled to the data processing system for displaying a location of the lost vehicle on the display device.
49. A system for locating a lost vehicle comprising:
50. The system for locating a lost vehicle as recited in claim 49, wherein the camera captures the image of the lost vehicle at an observation time.
51. The system for locating a lost vehicle as recited in claim 50, further comprising means, coupled to the data processing system, for determining a position of a predetermined measurement point at the observation time.
52. The system for locating a lost vehicle as recited in claim 51, further comprising means for concatenating the identifier, the observation time, and the position of the measurement point into a vehicle data record.
53. The system for locating a lost vehicle as recited in claim 49, further comprising a display device coupled to the data processing system for displaying a location of the lost vehicle on the display device.
54. A system for locating a lost vehicle comprising:
55. The system for locating a lost vehicle as recited in claim 54, wherein the first transponder receives the transmission at an observation time.
56. The system for locating a lost vehicle as recited in claim 55, further comprising a positioning system, coupled to the data processing system, for determining a position of a predetermined measurement point at the observation time.
57. The system for locating a lost vehicle as recited in claim 56, further comprising a concatenation system for concatenating the identifier, the observation time, and the position of the measurement point into a vehicle data record.
58. The system for locating a lost vehicle as recited in claim 57, wherein each data record further includes an observation time and a vehicle position.
59. The system for locating a lost vehicle as recited in claim 54, further comprising a display device coupled to the data processing system for displaying a location of the lost vehicle on the display device.
60. The system for locating a lost vehicle as recited in claim 54, further comprising a sensor capturing an image of the lost vehicle.
61. The system for locating a lost vehicle as recited in claim 60, further comprising the sensor coupled to the sub-
62. The system for locating a lost vehicle as recited in claim 43, wherein a lost vehicle is a vehicle that is:
63. The system for locating a lost vehicle as recited in claim 49, wherein a lost vehicle is a vehicle that is:
64. The system for locating a lost vehicle as recited in claim 54, wherein a lost vehicle is a vehicle that is:
BACKGROUND OF THE INVENTION
1. Field of the Invention
This invention relates to law enforcement and more particularly to an automated means for detecting vehicles that have been parked for longer than the legally prescribed period.
2. Background
Municipal governments enact regulations to govern the parking of cars along city streets. Typically, time limits are posted along each street and parking fines are levied on vehicle owners who park their cars for longer than the posted time. Two benefits result from the practice of making and enforcing on-street parking regulations:
1) Traffic congestion is reduced by forcing motorists parked for long periods to find suitable off-street parking arrangements, thereby vacating their more convenient, on-street parking spaces for use by motorists wishing to stop for short periods.
2) The parking fines levied on motorists who violate parking regulations create revenue the municipality.
In order to reap these benefits, the fundamental technical problem faced by Parking Authorities is how to detect when vehicles are in violation for the posted time limit. Heretofore, two violation-detection and enforcement technologies have been employed:
1) Parking meters
2) Timed chalk-marking of car tires
Enforcement Using Parking Meters
Parking meters are timing devices installed adjacent to each parking space that the Parking Authority wishes to enforce. Once installed, parking meters permit motorists to rent each on-street parking space for short periods. To rent the space, the motorist must insert coins into the meter, thereby starting a timer mechanism that suppresses display of an “Illegally Parked” flag. When the purchased parking period has expired, the “Illegally Parked” flag is again made plainly visible, thereby enabling a Parking Enforcement Officer patrolling the area to see at a glance that the parking space is illegally occupied. The officer continually inspects every parking meter along the patrol route and issues citations to those cars that are illegally parked.
Detecting parking violations with parking meters is an effective means of enforcing regulations, particularly in areas with high traffic density such as downtown commercial districts. A significant advantage of using parking meters to detect infractions is that they also provide a means for collecting a “pay per use” rental fee. The requirement to insert coins provides a continual stream of revenue to the municipality, even if no vehicle is ever cited for an over-parking infraction. However, each parking space requires its own parking meter, which is an expensive piece of equipment to purchase and install. The capital costs of initiating a parking metered enforcement program are considerable. Since the Enforcement Officer must visually inspect each parking meter along the route, patrolling the meters is a tedious, labour intensive activity that adds to the overall cost of metered enforcement. In congested, downstream areas, officers are often obliged to patrol the route on foot, thereby adding to the labor cost of the system. Maintaining the meters in good working order and emptying their contents is another significant expense related to metered enforcement.
Enforcement Using Timed Chalk-Marking of Car Tires
The high cost of installing, maintaining and patrolling parking meters limits their cost-effectiveness in many on-street parking situations. In particular, low-density areas outside the downtown core may be considered “not profitable enough” to warrant the use of parking meters. In these areas, the other method of parking enforcement commonly employed is “timed chalk-marking of car tires” (hereinafter referred to as “tire-chalking”).
Parking regulation enforcement using the tire-chalking methodology is as follows:
1) A route is chosen such that all the parked cars along it are subject to the same parking regulation (e.g. 2-hour parking limit). The Officer patrols the route and stops beside every parked car that's encountered. Typically, the patrol is done using a car however foot and bicycle patrols are also common modes of transportation.
2) A temporary mark is made on one of each car's tires using a piece of chalk or similar marking utensil. In order that the officer can attest to having made the mark, some effort is made to keep all the marks similar in size, color, shape and placement.
3) At regular intervals along the route, the time is noted, thereby the enabling a time to be estimated for when each of the chalk-marks was made.
4) After all of the cars parked along the patrol route have been marked, the officer retraces the same route. Care is taken to regulate the speed of the patrol such that the officer returns to the location of each of the chalk-marks just after the permissible parking period has expired (e.g., if the posted time limit is two hours, then the officer must return to the same location slightly more than two hours after chalk marks were made at that location).
5) During the second trip over the patrol route, the officer visually inspects the tires of each and every vehicle looking for a chalk-mark made during the previous circuit. A found chalk-mark serves as evidence that the marked vehicle has not moved during the period the Officer has been away patrolling the rest of the circuit.
6) When a chalk-marked car (i.e. an illegally parked car) is sighted the officer issues it a parking citation. After writing the details of the infraction onto the citation and attaching it to the offending vehicle, the Officer continues along the route, slowing down or speeding up as necessary to stay on-schedule for detecting subsequent parking violations.
The chalk-mark method of detecting parking violations is commonly used along lightly traveled streets where metered enforcement would not be cost-effective. Since no capital investment in parking meters is required to provide infrastructure, a tire-chalking enforcement program is less costly to initiate than an enforcement program based on parking meters.
Furthermore, tire-chalking provides a more flexible means of parking enforcement. Patrol routes can be quickly adapted to suite the changing parking habits that generally occur at different times of the day, on different days of the week or in different seasons of the year; something that meters cannot easily accommodate.
While the capital cost of using chalk-marks as a means to enforce parking regulations is less than that of using parking meters, the labor cost of using chalk-mark detection is significantly higher. The principal factor contributing to the workload is the need to manually mark every car along the patrol route . . . a task that is both physically demanding and time consuming.
Furthermore, the route must be patrolled twice before any infractions can be detected whereas parking meters guide the Officer to infractions every time the route is patrolled. The high labour cost of first applying chalk-marks and then searching for them significantly reduces this methodology's attractiveness as a parking enforcement means. Furthermore, the second traverse of the patrol route is often dedicated only to the inspecting tires and issuing citations, thereby permitting newly parked vehicles to go unmarked.
Furthermore, detection and prosecution is based entirely on the presence of chalk-marks on each vehicle. Vehicle owners can evade prosecution simply by hiding the mark. Typically, each tire is marked on its tread surface so simply moving the car a few feet within the parking space will rotate it away from the officer's view, thereby making it impossible to detect the infraction during the second traverse of the patrol route. If the chalk-mark has been made on the side of the tire rather than on its tread, the mark can still be easily rubber off to evade detection.
Regardless of whether parking regulations are enforced using parking meters or tire-chalking, once a parking infraction is detected, creating a legal citation and serving it on the vehicle's owner takes a considerable amount of time and effort. The main factor contributing to this workload is the requirement for the officer to write down all the details of the infraction by hand onto a paper citation from before affixing it to the offending vehicle (time, location, license plate number, nature of infraction, etc). Furthermore, the labour cost of processing each parking citation is increased by the requirement to transcribe the hand-written data into a computerized system that tracks the infraction through the court system.
Another factor that degrades the performance of both enforcement systems is their incapacity to detect “scofflaw” motorists. “Scofflaw” is the term commonly used by Parking Authorities for a motorist who flouts parking regulations. Scofflaws flour parking regulations by discarding or otherwise ignoring all parking citations they receive. Neither the parking meter enforcement methodology nor the tire-chalking enforcement methodologies can detect whether or not the vehicle's owner is likely to pay the fine levied for the infraction. Since many of the citations written by officers are ignored by scofflaw motorists, the inability of both the meter and chalk-mark enforcement methodologies to deal effectively with scofflaw motorists reduces their fiscal efficiency.
It is therefore the purpose of the present invention to provide a means of enforcing parking regulations that eliminates the drawbacks inherent to using either parking meters or tire-chalking.
LPR Technical Background
The present invention exploits “Optical Character Recognition”. OCR image analysis is a well-established technology that has many applications in the publishing and archiving industry. Essentially, OCR is an image analysis process that converts a raster-scanned image of printed characters into machine readable ASCII codes, thereby eliminating the need to re-type old documents into a computer and rendering them amenable to automated processing.
One common application of OCR technology is to digitize a vehicle's license plate number from its raster image. When applied to vehicular imagery, OCR technology is commonly referred to as “License Plate Recognition” (LPR). Heretofore, LPR has been applied to stationary law enforcement and security applications (e.g. identifying vehicles in controlled areas such as parking garages). LPR technology has also been successfully applied in revenue collection applications (e.g. automatic billing of motorists using toll highways).
LPR is a complex process that is well documented in the literature and prior art. Various aspects of LPR methodology and terminology are relevant to the present invention and therefore merit summary description.
Essentially, LPR is comprised of three operations that are sequentially applied to the vehicle's raster image. These processes attempt to progressively refine the complex, unique identification of the vehicle captured in the raster image into an alphanumeric string of text identical to the text inscribed on the vehicle's license plate. Since this alphanumeric string of test is compact, easily comprehended and legally linked to the vehicle's owner, its correct extraction from the raster image is the ultimate goal of LPR. The interim digital encapsulations of the raster image that are part of the LPR process are less desirable however they also uniquely identify the vehicle in a way that has been exploited in certain LPR applications. The interim encapsulations of LPR are analogous to a person's fingerprint while the end product of LPR (the license plate number) is analogous to the same person's name.
The three conceptual steps that comprise LPR are:
Vectorizing the raster image (hereafter referred to as creating the “vector-model”)
Step 1)Isolating only those vectors that describe the license plate within the vector-model (hereafter referred to as creating the “plate-model”)
Step 2)Recognizing the alphanumeric characters in the plate-model (hereafter referred to as creating the “plate-string”)
The three steps that comprise LPR can be summarized as follows:
Step 1) Vectorizing The Raster Image:
Discrete physical objects depicted in a raster image will generate zones within which all the pixels share similar color or gray-scale values. Vectors are mathematically defined lines that trace the perimeter of these zones. Some LPR algorithms make use of the aggregation of pixels inside these zones rather than their perimeter however for the purpose of this summary, they can be considered the same geometric entities. Before tracing the outline of these zones, spatial filtering algorithms are applied to the raster image to compensate for the effects of extraneous pixel noise (such as varying color caused by precipitation, dirt on the vehicle, slight variations in paint color, etc). The object of vectorization is to identify and group only those pixels that correspond to real physical objects portrayed as discrete visual features in the raster image. In the case of a parked car's raster image, the desired vectors follow the silhouettes of the various mechanical parts and visual features that comprise the car (windows, fenders, bumpers, license plate, license plate text, dirt on license plate, etc). The vectorization algorithm may also outline discrete elements in the visible background scenery (sidewalk, trees, pedestrians etc.).
Spurious shadows in the vehicle's raster image will degrade the spat ial fidelity of vectors extracted from it. Therefore, many LPR systems improve their performance by illuminating the scene with supplementary lights, to minimize shadow effects in the image presented to the vectorization algorithm.
The set of all vectors extracted from a raster image using a particular algorithm constitutes a unique “digital fingerprint” for the scene in the ima ge. This unique identifier is hereafter referred to as the image's “vector-model”. A vector-model generally occupies less storage space than the raster image from which it is derived. In addition, since the points and lines in the vector-model are mathematically defined entities, they lend themselves to the rapid computations required in steps 2 and 3 described below.
Step 2) Recognizing the License Plate Within the Vector-Model:
Algorithms are then applied to the image's vector-model to isolate only those vectors or zones of similar pixels that describe the license plate's physical structure. This unique “digital fingerprint” of the license plate is hereafter referred to as the “plate-model”. Different algorithms could be applied to the vector-model to try to isolate other physical structures (the “bumper-model” the “window-model” etc). However, for typical applications, the license plate is the physical object of greatest interest, therefore the plate-model is the subset searched for within the image.
The rectangular shape of a license plate provides one criterion for testing if a candidate subset set of vectors is indeed the plate-model. However, there will typically be many vectorized rectangles in the vector-model that complicate isolating the plate-model (dealer logos, bumper stickers, parking permits, decorative trim etc.). Therefore, multiple geometric and stochastic tests are typically made on all candidate plate-models in order to rank their probability of being the correct one. When one of the candidate plate-models achieves a sufficiently high probability of modeling the real license plate, it is passed on to step 3 (described below).
Some LPR implementations only vectorize a subset of the total raster image and create the plate-model directly. Various methods have been used to directly localize the plate. One approach is to exploit the reflective paint used on many license plates. The plate's reflective surface can be used to localize it within the image without the need to vectorize other physical elements in the scene. Different LPR manufacturers use different terminology for the image's interim states as it is prepared for recognition of the license plate's alphanumeric characters. For the purposes of the present invention, the end product of LPR (the license plate number) as well as its precursor stages (referred to here as the raster image, the vector-model and the plate-model) are all encompassed within the term “unique vehicle identifier”.
Step 3) Recognizing the Alphanumeric Characters in the Plate-Model:
The plate-model is then analyzed to transform the vectorized zones within its perimeter into an alphanumeric string of characters that spell out the vehicle's license plate number. The recognized string of text that estimates the vehicle's license plate number is hereafter referred to as the “plate-string”.
Typically, before attempting to recognize the plate-string's characters, the distortion caused by an oblique camera angle is geometrically rectified. This geometric rectification procedure is generally referred to as “de-skewing”. Since character recognition is based on analysis of the plate-model's geometry, de-skewing the perspective distortion of the vectorized zones will improve the accuracy of the character recognition algorithm.
Typically, one of three OCR methodologies is used to recognize each character of the plate-string from within the plate-model. “Structural analysis”, “pattern matching”, and “neural networks” are the terms commonly used for these algorithms. Each of these complex algorithms is well documented in the literature and has its unique advantages and disadvantages. Some LPR systems use combinations thereof to improve the reliability of the characters recognized from the plate-model.
To improve the reliability of character recognition, the LPR algorithm must also be customized to accommodate the different fonts, color schemes and character syntax's appearing on the plates issued in different transportation jurisdictions.
Full Recognition Mode LPR
The sequential 3-step algorithm described above is commonly known as “Full Recognition Mode LPR”. Full Recognition Mode LPR algorithms cannot recognize the plate-strings of all observed vehicles with 100 percent accuracy. However, for some applications a certain number of plate-string errors is acceptable. For example, it is acceptable that a certain percentage of vehicles passing through a toll plaza not be correctly recognized (and thereby escape being billed the toll charge). Unrecognizable plates can be tolerated if the algorithm is at least able to compute that its best estimate of the plate-string is not sufficiently reliable, thereby permitting the enforcement system to simply ignore those “difficult” plate readings.
Pattern-Matching LPR
Some other LPR applications demand a very high degree of certainty that certain vehicles will be recognized. For example, a security camera might be setup to control access to a parking garage. In this scenario, it may be imperative that only authorized vehicles are permitted to enter and furthermore, that those vehicles are always allowed to pass. To deal with this requirement an algorithmic subset of Full Recognition Mode LPR known as “Pattern-matching LPR” is commonly employed.
“Pattern-matching LPR” doesn't rely on complete recognition of the alphanumeric plate-string to identify a vehicle. Instead, Pattern-matching LPR stops short of estimating the plate-model and simply compares (matches) the two vector-models (patterns) that are derived from two captured raster images. If the mathematical correlation between the two vector-models is sufficiently high then the algorithm concludes that the two images deficit the same vehicle.
In the access control example given above, the vector model's of all authorized vehicles would be captured a priori and stored in the system's database, thereby permitting the Pattern-Matching algorithm to refer to the known vector-model of all authorized vehicles that request entry to the garage. The vector-models of all unauthorized vehicles will not correlate to any of the authorized vector-models and can therefore be denied access to the garage.
Conceptually, Pattern-matching LPR is the same as performing Full Recognition Mode LPR on the two raster images and then correlating the two computed plate-strings to see if they contain the same text (pattern). However, Pattern-Matching LPR has one important advantage over Full Recognition Mode LPR; since a plate model contains more mathematically defined spatial information about the vehicle than a fully recognized plate-string, the correlation computed between two vector-models is less vulnerable to vectorization errors than the correlation of two fully recognized plate-strings. Pattern-matching LPR is therefore more reliable at determining if two raster images portray the same vehicle. However, Pattern-matching LPR cannot extract useful information from a single image and cannot make the legal link to the vehicle's owner (only Full Recognition Mode LPR can provide that information).
SUMMARY OF THE INVENTION
The present invention overcomes the problems associated with enforcing parking regulations by automating the manual processes performed by a Parking Enforcement Officer using the “tire-chalking” enforcement methodology described above.
Instead of manually applying chalk-marks to each vehicle, a digital camera captures a raster image of each vehicle along the patrol route thereby identifying it as being present in its observed location. A License Plate Recognition algorithm immediately extracts a unique digital identifier for the vehicle depicted in each raster image and stores it in a computerized database. Typically, the unique vehicle identifier is the alphanumeric text appearing on the vehicle's license plate (the “plate-string”). Each of the observed unique vehicle identifiers is stored in a database record that also contains the time it was observed and its geographic location. The time-stamp of each vehicle ID is typically read from the computer's internal clock while its geographic location is typically read from an external positioning system such as the “Global Positioning System”) (GPS).
The driver of the patrol vehicle traces and re-traces the patrol route in a manner similar to an officer first applying chalk-marks to all parked cars along the route and then later searching along the route for marked cars that are over-parked. Each time a new image of a parked car is captured and its unique identifier has been determined, the computer searches its database to see if that vehicle identifier has already been observed by the system. If a matching vehicle identifier is found in the database, the computer compares the time and location of vehicle's first observation to the time and location of its second observation. If this comparison reveals that the vehicle has been parked at the same location for longer than the local parking time limit, then the computer sounds a “parking violation alarm” that commands the driver to stop the patrol vehicle. The system then prints out a legal parking citation describing the evidence of the infraction. The officer then visually verifies the evidence, signs the citation, affixes the citation to the offending vehicle and continues along the patrol route.
In an alternate embodiment, the same measurement technique is employed to determine the period of time each vehicle has been parked. However, instead of simply testing to see if that period is longer than the local parking regulation permits, the system uses the time parked to determine a parking fee and charge that fee to the vehicle's owner.
BRIEF DESCRIPTION OF THE DRAWINGS
DETAILED DESCRIPTION
Reference will now be made in detail to preferred embodiments of the invention, examples of which are illustrated in the accompanying drawings. Wherever possible, the same reference numbers will be used throughout the drawings to refer to the same or like parts.
Computer system
The computer system
A digital video camera
A proximity sensor
A positioning sub-system
A clock
A printer
In a preferred embodiment described below, a radio frequency vehicle identification transponder
In preferred embodiments described below, a data link
Data Collection
In a preferred embodiment of the invention, the capture of each license plate image is initiated manually by an operator who aims and triggers the video camera when the license plate transits the center of the camera's viewfinder.
In another preferred embodiment, the capture of license plate imagery is triggered automatically without the need for manual aiming by an operator. To provide this function, an electronic “proximity sensor”
In a preferred embodiment, the proximity sensor measures the changing distance between the patrol vehicle and the sides of parked cars
To optimize its view angle onto each license plate
While the proximity sensor
In another preferred embodiment, the proximity of each new vehicle
Once the camera
Sometimes, the rear vehicle
In another preferred embodiment (not illustrated), a plurality of distance sensors
In a preferred embodiment, the baseline distance between the proximity sensor
The higher the video camera
In another preferred embodiment, when the camera's frame grabber is triggered, it fires rapidly in succession, thereby capturing a series of images of the same license plate
In another preferred embodiment, all the video imagery observed by the camera
Another physical characteristic of cars parked in areas that permit side-by-side parking is exploited by the present invention, thereby improving the efficiency with which Epoch-IDs are observed. The roadway separating rows of cars parked side-by-side is often free of the on-coming traffic, which (on public streets) constrains the patrol vehicle to observe cars parked along only one side of the street. To observe all of the cars parked along both sides of the roadway therefore requires two circuits of the patrol route (one in each direction). If however the patrol vehicle has unobstructed use of the roadway (such as in private parking facilities or lightly traveled public streets) then greater efficiency is obtained by observing the parked cars along both sides of the roadway at once (effectively doubling the productivity of each patrol vehicle).
Therefore, in a preferred embodiment (not illustrated), the patrol vehicle
At the same instant that each vehicle's raster image is captured by the video camera
The geographic coordinates observed by the positioning sub-system
Data Processing
Recognition of Each Vehicle's Unique Identity
As soon as a parked car's geo-referenced and time-stamped raster image of has been captured into the host computer
In preferred embodiments of the present invention, Pattern-matching LPR and Full Recognition Mode LPR are applied to the captured imagery (both methodologies are summarized above in the “technical background”). In preferred embodiments (described below), the two LPR methodologies are applied both separately and in concert, thereby optimizing t he system's flexibility and reliability.
The LPR sub-system
At the same instant that each vehicle's raster image is captured and a unique vehicle identifier is extracted from it. The system's computer also captures the current time and the current geographical coordinates observed by the positioning sub-system. These three observed data (time, location and unique vehicle identifier) are concatenated and stored as a data record in the system's database. Each of these data records is hereafter referred to as the parked vehicle's “Epoch-ID”.
Storage and Use of Data Reliability Indicators
in a preferred embodiment, statistical reliability indicators for each of the three observed data in the Epoch-ID are computed and stored along with their respective data element (i.e. the reliability of each time observation, position observation and vehicle identity estimate are recorded). These reliability indicators are subsequently used to enhance the performance of the real-time violation detection algorithm
1) The Epoch-ID's Temporal Reliability Indicator:
Since infractions are detected by subtracting two time-stamp observations, the absolute accuracy of each Epoch-ID's time-stamp is not critical (gross clock errors will be subtracted out by the detection algorithm). Therefore, as long as the difference between the two Epoch-ID's time-stamps can be shown to be accurate to within a few seconds, the temporal component of the parking infraction detection algorithm will remain sufficiently accurate. The time sensor
In a preferred embodiment, the system's position sensor
2) The Epoch-ID's LPR Reliability Indicator:
Many aspects of LPR are stochastic processes. An estimate of the statistical reliability of each recognized plate-string is therefore a by-product of most Full Recognition Mode LPR algorithms. If a Pattern-matching LPR algorithm is applied to the imagery, the computed correlation factor between the two matched vector-models is also a good statistical reliability indicator. Therefore, in a preferred embodiment, a statistical reliability estimated for the plate-string and/or the pattern-match is included in each stored Epoch-ID.
To reduce the chance of false alarms from the parking violation alarm, the Officer can link its sensitivity of the to the computed reliability of the LPR process. In a preferred embodiment, the violation alarm's sensitivity is adjusted by varying a threshold value used to reject LPR data that is not considered sufficiently reliable. For example, the database software
The plate-string recognized in each captured image is the most succinct digital encapsulation of the vehicle's unique identity that can be derived from the LPR process. The plate-string is therefore the best search criteria for quickly searching the database to detect parking violations. Furthermore, the plate-string (the license plate number) can also be used to link into useful external database
While the plate-string has the advantage of being concise, it's the entire raster image from which the plate-string is derived that is each vehicle's most complete and unique identifier. Therefore, in a preferred embodiment, the vehicle's raster image is also stored as part of the Epoch-ID and archived to enhance the system's reliability. These images may eventually serve as evidence in court.
The stored raster imagery also serves as a means for independently verifying the validity of the LPR process. In a preferred embodiment, whenever the violation alarm is triggered, the system immediately displays the two raster images contained in the Epoch-IDs found to match by the database software
Both the vector-model and the plate-model computed by the LPR algorithm are essentially less verbose, encapsulations of the same unique vehicle identifier captured in the raster image. Therefore, in a preferred embodiment, each raster image's vector-model and/or plate-model is also stored in the Epoch-ID. By retaining the vector-model and/or plate-model, they become available for re-analysis to either: confirm the plate-string, correct the plate-string or provide an alternate method of matching Epoch-IDs. The analytical exploitation of interim LPR data is performed by one of three possible embodiments:
1) In a preferred embodiment, the Enforcement Officer exploits the vector and/or plate models by plotting them onto the system's computer display screen. The Officer inspects the image of the license plate to confirm the violation prior to signing the parking citation and serving it on the offending vehicle, (similar to the procedure described above for verifying the plate-string by inspection of the raster images). Since vector-models occupy less storage space than raster images this embodiment eases the load on the system's computing resources.
2) In a preferred embodiment, near real-time re-analysis of the captured vector models is carried out by a second LPR algorithm prior to issuing a violation alarm. This embodiment would also be used as a means to reduce the load on the system's computing resources. For example, a first Full Recognition Mode LPR algorithm that is very fast but somewhat less reliable would be used to initially detect suspected violations. Once a suspected violation is detected by the first LPR algorithm, a second, more reliable but slower executing Full Recognition Mode LPR algorithm, would be applied to the same data, thereby improving the reliability of the citation. Only after the two Epoch-IDs have passed the second, more rigorous, extraction of matching plate-strings would the violation alarm be triggered to alert the system's operator to stop the patrol vehicle and issue a citation.
3) In a preferred embodiment, the two vector-models stored in suspected Epoch-IDs flagged by Full Recognition Mode LPR are then submitted to a Pattern-matching LPR algorithm. Since the results of a pattern-match are inherently more reliable than the results of a plate-string, confirmation by this re-analysis of the data adds to the reliability of citations issued by the system.
3) The Epoch-ID's Spatial Reliability Indicator
The statistical reliability of the geographical positions used in the violation detection algorithm are also quality indicators that add weight to the body evidence that may eventually be presented in court. Therefore, in a preferred embodiment, an estimate of each observed position's probable error is made and included in the Epoch-ID.
The present invention can use an “off the shelf” positioning system to provide geo-referencing information for each Epoch-ID. Satellite systems such as the Global Positioning System (GPS) are the preferred sub-system. Other sub-systems such as LORAN, GLONASS, differential GPS, GPS/inertial, GPS/different-odometer, GPS/different-odometer/fluxgate-compass and GPS/map-matching are also acceptable sub-systems. The integration of GPS with other sensors is a common practice that is useful in areas of poor satellite visibility (such as near high buildings). All of the above positioning system configurations are well-established technologies that, due to redundant range observations, lend themselves to real-time error estimation. In a preferred embodiment, the positional error estimate provided by the geo-referencing sub-system is stored in the Epoch-ID to provide Quality Control of the spatial parameter.
The geographic coordinates observed by the positioning sub-system define the position of the patrol vehicle rather than that of the (nearby) parked cars under surveillance. The parking violation detection algorithm described below assumes that the coordinates logged in the Epoch-ID describes the position of each parked car. Therefore, in a preferred embodiment, the distance observed from the patrol vehicle to each parked vehicle (measured by the range finder) and the known geometry between the location of the camera, the range finder and the positioning system's antenna, is used to compute the geographic coordinates of each parked car's license plate. The estimated position of each license plate (and its estimated uncertainty) is then stored in the parked vehicle's Epoch-ID.
Embodiments That Use Full Recognition Mode LPR
As soon as each Epoch-ID has been entered into the database, the computer searches to see if the same plate-string has been observed previously by the system
If a previous instance of the plate-string is found in the database of Epoch-IDs
If the difference between the two observed positions reveals that the vehicle has not moved an appreciable distance since its previous observation, then the system flags the vehicle as being under suspicion of violating the parking regulations
In a preferred embodiment, the positional test for whether or not the observed vehicle has moved “substantially” includes a distance tolerance to compensate for the instrumental error inherent to the positioning sub-system
The test criteria for determining if the vehicle has moved an “appreciable” distance may also include a required radius of movement stipulated in municipal parking regulations. For example: a municipality's parking regulations might stipulate that a car must be moved at least 200 m in order to “restart” its legal parking status. Including this 200-meter radius in the positional tolerance insures the motorists who have not moved their vehicle the required distance will be flagged for a parking citation.
When a suspect vehicle has been flagged
The system then searches a database of geo-referenced parking regulations (described below) to determine the legal time limit that's applicable to the parked vehicle's present location
If the elapsed time is greater than the time period legally permitted for that location, then the system declares a parking violation
When the system declares a parking violation, a “parking violation alarm” is triggered
In a preferred embodiment, when the parking violation alarm is triggered, the computer
After the LPR vehicle identification has been independently verified, the system uses the output device
1) The two matching plate-strings (license plate numbers) that were recognized from the two digital images of the (same) parked car
2) The time that each of the two license plate numbers was observed. The elapsed time as well as the permitted parking period may optionally be printed onto the citation.
3) The geographical coordinates of the two independent sightings of the parked vehicle
4) The in formation needed to describe the local parking regulation that has been contravened (the parking time limit at the time of the infraction, the street name where the infraction occurred, etc.).
As soon as the parking citation has completed printing (typically the time it takes to stop the patrol car
Embodiments That Use Pattern-Matching LPR
When a pattern match is found, the system tests to see if the two matching Epoch-IDs were observed at substantially the same location
The Officer then stops the patrol vehicle however the offending parked vehicle's license plate number is still unknown and must therefore be identified manually. To facilitate identifying the vehicle, the system displays the two raster images and/or vector-models that triggered the alarm. The Officer then visually inspects the images to verify that the vehicle captured at the first and second observation epochs appears to be the same make and model of car. If the violation alarm passes the visual inspection, the Officer then reads and enters the alphanumeric characters on the vehicle's license plate into the system where it becomes the plate-string inserted into each of the Epoch-Ids
In another preferred embodiment (not flow-charted), the Pattern Matching LPR method is first used to detect a suspected parking infraction. Once the violation alarm has been sounded, the system applies then the less reliable (but more useful) Full Recognition Mode LPR algorithm is applied to the two images of the vehicle's license plate. If the plate-strings extracted from the images are identical then the imagery and alphanumeric data are displayed to the Officer for visual inspection and certification. If the plate-string does not match the Officer's visual interpretation of the imagery (for example, the numeral “zero” may have been recognized as the letter “O” by the LPR algorithm) then the Officer edits the plate-string. Since the plate-string will, in most cases, be correct the Officer's data input workload is thereby reduced.
The database search to Pattern-Match plate-models
The distance criterions used to search for “nearest neighbor” Epoch-IDs will typically be the same distance criterion used by the violation detection algorithm to test if a vehicle has moved “appreciably” between observations (described above). The nearest neighbor distance criterion may therefore be comprised of a distance to compensate for instrumental noise in the positioning system as well as a regulated distance imposed to force motorists to completely vacate the vicinity after legally occupying a parking spot.
Therefore, in a preferred embodiment, the database search
In another preferred embodiment (not illustrated), the Full Recognition Mode LPR methodology is first applied to all captured imagery. If however the estimated reliability of either of the plate-strings matched in step
Embodiment That Uses Radio Frequency Transponders For Vehicle Information
The embodiments described above rely on LPR technology to uniquely identify each of the parked cars along the patrol route. LPR has the significant advantage of being an entirely passive means of determining the unique identity of vehicles (i.e. all registered vehicles are already equipped to be identified). To facilitate new highway applications such as automated toll collection, there is a growing trend to provide an active means for identifying each vehicle (i.e. means that demand a component be affixed to each vehicle in order for it to be recognized). Various means have been developed for actively identifying vehicles. Bar codes imprinted onto each vehicle have been proposed. The bar codes are subsequently optically scanned to determine the vehicle's identity and are therefore functionally equivalent to LPR of the vehicle's alphanumeric license plate number. Magnetic encoding strips affixed to each registered vehicle has also been proposed to as a means for actively identifying vehicles.
Another proposed means for actively identifying each vehicle is provided by a low-power radio-frequency transponder affixed to each vehicle monitored by the system. Each mobile transponder responds to a low-power interrogation signal broadcast from stationary highway infrastructure (such as an automated tollbooth). The low broadcast power of the mobile and stationary transponders limits their range such that each mobile transponder is triggered only when it is in close proximity to the stationary transponder. When interrogated, the vehicle's transponder responds by broadcasting a low-power radio-frequency data signal that contains the vehicle's unique identification code (e.g. its license plate number). In the future more and more vehicles will be equipped with active identification means such as transponder, therefore transponder technology provides a viable alternative to using LPR technology in the present invention.
In another preferred embodiment (not illustrated), the present invention employs both LPR and transponder technology to observe each parked car's unique vehicle identity along the patrol route. Vehicle's without transponders are identified using LPR as described above. However, those parked vehicles equipped with a transponder will respond to the patrol vehicle's interrogation, thereby providing a redundant vehicle identifier that the system adds to those vehicles' Epoch-IDs. This redundant vehicle identifier adds to the body of evidence used to support claims against the vehicle's owner.
Embodiment That Enables Charging Pay Per Use Fee
The preceding embodiments emulate the tire-chalking methodology commonly used to detect illegally parked cars and levy to financial penalty on their owners. This “penalty mode” of parking enforcement cannot accommodate the collection of modest rental fees from vehicles that are legally parked for short periods. This shortcoming dictates that most motorists under surveillance by the system will in fact park for free (as long as they do not park longer than the arbitrary time limit). This is a serious financial drawback when compared to parking meters. Parking meters require motorists to insert coins, thereby providing a continuous revenue stream to the municipality, even if no cars are ever convicted of over-parking.
The tire-chalking methodology lays the entire financial burden of the system on tho se few motorists caught over-parking and this contributes to a public perception of unfairness. For example: two motorists park in the same two-hour zone at the same time. The first motorist returns one minute before the patrol vehicle makes its second round and thereby escapes without having paid any parking fee. The second motorist arrives only two minutes later to find a $25 parking citation. This perceived unfairness adds to the psychological stress endured by all motorists.
Therefore, in a preferred embodiment, the present invention provides means that enable the Parking Authority to collect modest rental fees from those vehicles that are legally parked for short periods along public streets (in a manner analogous to that used in the parking meter enforcement methodology). The net effect of using this embodiment is to transform all of the real estate along the municipality's streets into a “pay per use” parking lot without having to install and maintain physical parking meters.
To provide the necessary revenue collection means, the previously described embodiments that emulate tire-chalking enforcement are linked to a central, computerized billing system
Motorists (hereafter referred to as “clients”) wishing to making use of the “pay per use” parking service must enter into an agreement giving permission to the Municipal Parking Authority to withdraw funds from the client's electronic banking facility
To improve fee collection performance, the frequency at which the patrol vehicle
If the database search
In a preferred embodiment, observed vehicles that have not been registered as clients of the system are initially served with a printed warning to do so or be faced with being served a punitive parking citation in the future. Repeated warnings may escalate in their aggressiveness from “polite reminder” to “final notice”. The central database maintained by the Parking Authority
If however the database search
Each newly initialize “virtual-parking-meter” is a database record containing the cumulative evidence that a client vehicle has been observed parking at a particular geographic location at a particular time. Each virtual-parking-meter therefore also contains a data field to contain the elapsed “time-showing” since the vehicle was first observed to be parking at that location. The virtual-parking-meter's “time-showing” field is therefore initialize to zero.
On subsequent circuits of the patrol route, each time the patrol vehicle observes the same client vehicle and verifies that it has not moved
If the patrol vehicle's second visit to the location of an existing virtual-parking-meter reveals that the vehicle's unique identifier has changed from that observed on the previous circuit, then the algorithm assumes that the previous occupant was parked for less than the patrol vehicle's observation period (step not illustrated in FIG.
During subsequent circuits of the patrol route, each time the patrol vehicle passes in close proximity to the location of an existing virtual-parking-meter, the identity of its tenant vehicle is observed. If the same vehicle identifier is observed as on the previous circuit
When the patrol vehicle eventually observes that a virtual-parking-meter has been vacated by its tenant vehicle
In a preferred embodiment, the parking fee structure charged per unit time is defined so as to encourage motorists to respect the Parking Authority's traffic management priorities. For example, a client parked in a busy commercial district might be charged $0.25 for the first half-hour, $0.50 for the second half-hour, $5.00 for the third half-hour and $20.00 for parking past the local parking limit. This type of non-linear fee structure effectively encompasses the punitive penalty heretofore imposed on vehicles observed parking for long periods in areas where the Parking Authority wants to encourage short-term parking.
Various fee structures may be stored in the database
In a preferred embodiment, if an offending vehicle displays a handicap sticker that exempts the vehicle from parking regulations, the system verifies in the database to see if that vehicle is registered to a legitimate handicapped motorist. If fraud is suspected, the Officer may choose to wait for the motorist to return to the vehicle and then take appropriate action.
In a preferred embodiment, if the parked vehicle is registered to a car rental agency, the parking fee is automatically transferred to the car rental agency's electronic banking means so that the fee can be added to their client's rental account.
In a preferred embodiment, out of town or out of state vehicles detected by the system may be issued with a citation thanking them for visiting the municipality and wishing them a pleasant visit.
Linking the sensor data observed by the present invention to an external electronic billing means provides a number of advantages. By automating all transactions, the cost of administering the enforcement system is reduced. Furthermore, direct electronic fund withdrawal renders the enforcement system more resistant to scofflaw motorists. Furthermore, since the present invention reduces the overall cost of parking enforcement, those cost savings can be applied to reduce the total amount each must client pay for on-street parking. Furthermore, the time-consuming tasks of feeds coins into a parking meter (or paying citations for over-parking) are eliminated, thereby providing a more convenient parking experience of each client motorist.
Embodiment That Exploits a Fleet of Public Transit Vehicles
The embodiments described above typically maintain the observed Epoch-IDs in a database that resides on-board each mobile patrol vehicle (i.e the data link
However, the data link
Therefore, in a preferred embodiment (not illustrated), each patrol vehicle contributes all of its observed Epoch-IDs to a remote database (by means of either a real-time or post-mission link
The advantage of pooling all data observed by the entire fleet of patrol vehicles is that different patrol vehicle's can be used to observe each of the sequential Epoch-IDs used to augment the “time-showing” on each client's “virtual-parking-meter”. Since different patrol vehicles can be used to augment each parked car's virtual-parked-meter, the frequency with which each parked vehicle is observed is effectively increased. For example, a patrol vehicle might traverse a long route that only permits it to observe all parked vehicles once every 2 hours. However, if eight surveillance vehicles patrol that same route (while maintaining approximately equal spacing between them) then each parked car along the route would be observed approximately once every 15 minutes. Concatenating all of the Epoch-IDs observed by all eight patrol vehicles would permit each parked vehicle's virtual-parking-meter to be re-observed and updated once every 15 minutes.
This operational scenario provides significant cost savings when the patrol vehicles used to observe parked cars are Public Transit vehicles such as buses. Each bus is already traversing a pre-defined route as part of its primary function (transporting pedestrians along the bus-route). Each bus also passes in close proximity to all vehicles that are parked along the bus route. If each bus is equipped with the sensors and computing hardware described above, and all of the buses' observe Epoch-IDs as they perform their primary function, then batch processing their data sets will produce revenue from all of the vehicles parked along the route. Motorist parking along the bus routes would thereby effectively subsidize the Public Transit system.
Therefore, in a preferred embodiment, each vehicle in a fleet of patrol vehicles (such as Public Transit vehicles) observes Epoch-IDs and stores them in a central database. The accumulated data is periodically batch-processed such that parking fees are computed for all virtual-parking-meters observed along each route and collected in the manner described above.
Embodiments That Collect Revenue in Private Parking Facilities
Heretofore, owners of off-street parking facilities have been obliged to provide complex and expensive means for collecting parking fees from their clients. Typically, they control access to their private property in order to insure that parking fees are collected. An entry gate is used to prevent vehicles from entering their facility until the motorist has received a time-stamped entry-ticket. A second gate prevents vehicles from existing facility without paying the appropriate parking fee. Typically, a cashier at the exit gate collects the parking fees. The cashier receives the motorist's time-stamped entry-ticket, computes the fee corresponding to the time elapsed since the entry-ticket was issued and then lifts the exit gate once the parking fee has been paid by cash transaction. Providing these fee collection means contributes significantly to the operational cost of private parking facilities.
To use this embodiment, an administrative agreement is made between the owner of the parking facility and the municipal Parking Authority. The effect of this agreement is illustrated in step
In order to minimize financial losses caused by non-client vehicles parking in the facility, the patrol vehicle may impose more stringent measures against non-client motorists than when non-clients are encountered on public streets
To enable the Parking Authority's patrol vehicles to observe vehicles in off-street facilities, suitable modifications are made to the sensor system used to trigger data collection
Similarly, while the patrol vehicle is operated within an enclosed parking facility (above or below ground) its positioning sub-system
Embodiment That Provides a Locator Map to Client Motorists
One inconvenience often encountered in large parking facilities is that motorists cannot remember where they parked their cars. This problem is particularly acute in large parking lots at major airports, where motorists often leave their car parked for many days. After returning from their trip, many motorists have difficulty remembering where their car is parked. The present invention provides a solution to this problem by capturing both the location and identity of all vehicles in the parking facility during the course of its patrols. This spatial information can be displayed on a map display (described below) such that a client who cannot remember where their car is parked can see it plotted on a “locator-map” of the parking lot.
Therefore, in a preferred embodiment (not illustrated), the same system used to establish and patrol virtual-parking-meters in a large parking facility is made available for consultation by clients who have lost track of their parked vehicles. Typically, the client motorist goes to a kiosk where the system
The “locator-map” can also be output on the printer
In parking facilities that are already equipped to collect fees by conventional means (entry/exit gate, time-stamped entry tickets and cashier-booth), the present invention would not normally be required as a means of collecting parking fees. However, the “locator map” function described above might still be considered a valuable enough customer service to warrant use of the present invention. In those facilities, collecting fees from virtual-parking-meters would not be the purpose of the patrols: the “locator map” would be the sole information product produced by the system. Since “time-showing” on “virtual-parking-meters” would not need to be established and continually updated, the frequency of observation patrols could therefore be reduced considerably. For example, instead of a half-hour patrol frequency, a six-hour patrol frequency might be considered adequate for providing locator-maps to clients. Alternatively, the parking lot management might elect to perform more frequent patrols simply as a means of increasing surveillance and improving security for their clients. In the event of theft or vandalism within the facility, the present invention's video record of where and when license plates were observed within the facility would make it useful in deterring crime.
In another preferred embodiment, the “locator-map” function describe above is provided to clients on a mobile platform (rather than forcing the clients to make their way to an information kiosk to query the system). The most efficient means of providing this service to the clientele is to permit the operator of a patrol vehicle
Embodiments That Provide Navigational Guidance to the Operator
In a preferred embodiment, the positioning sub-system
To plan an optimal patrol route, the Officer uses the map display and a computer-pointing device to choose a series of waypoint locations that define a patrol route. The external database used to control the displayed map imagery contains geo-referenced information on the location and name of each street however it may also contain more detailed information such as: direction of one way streets, local speed limits, location of municipal infrastructure etc. Knowledge of the location of municipal infrastructure is used to test if an observed vehicle is parked too close to a fire hydrant, loading zone, driveway etc. The database of such information is commonly referred to as a “Geographic Information System” (GIS). The GIS is accessed by the system's computer
The map database also contains geo-referenced information describing the different parking regulations that apply along each street at different times of the day. Geo-referenced information on local parking regulations is required by the fundamental algorithmic test
For violation detection purposes, it's important that the Officer be able to navigate the patrol vehicle
Therefore, record keeping and route prediction means are built into the guidance system software
Furthermore, as the chosen route is being followed, the system monitors progress in real-time to see how well the driver is following the schedule (i.e. Arriving “just-in-time” for the second license plate observation). Based on how well the planned route schedule is being adhered to, the systems provides suggestions to increase speed, decrease speed or alter the pre-defined route.
The present invention provides a parking regulation enforcement system that can withstand legal challenges. Its inherent legal strength is due to two characteristics.
1) The detection of violations is inherently biased towards the accused motorist's presumption of innocence. Any errors in reorganizing a vehicle's unique identity will favour non-detection of a parking violator rather than false accusation. An incorrectly recognized license plate number cannot be flagged as a violation by the algorithm because it won't be able to find a matching Epoch-ID in the database. This bias towards the innocence of violators may result in a small number of real parking violators being missed by the system however it also insures a high probability that all detected parking violators are in fact guilty as charged.
2) Because the reliability of each Epoch-ID's data elements is statistically qualified, the evidence presented in court to corroborate the accusation is demonstrably sound. To add further legal weight to each accusation, the Parking Control Officer can testify to having visually verified that the license plate number recognized by the system was identical to that of the accused vehicle.
The present invention reduces the labor costs involved in enforcing parking regulations. A single patrol vehicle can enforce regulations over a much larger area than is possible when using manual enforcement techniques (i.e. manually marking each vehicle's tires and issuing hand written citations). The productivity gain afforded by the system results in increased revenue for the municipality from the same number of parking enforcement staff Alternatively, the increased enforcement productivity can be used to reduce the punitive fine levied for each parking infraction. The computerized nature of the present invention supports efficient data management through the court system without the requirement to digitize hand-written citation forms. When linked to “electronic banking” means, this centralized data management capability supports the collection of timed “pay per use” fees for short term parking, thereby emulating the functionality of parking meters.
Other Law Enforcement Applications
The data collected for the purpose of parking enforcement also has a number of other law enforcement and public service applications. Each vehicle observed by the system can be searched for in various crime-related databases
1) Identifying legally parked vehicles that have been reported stolen.
2) Identifying legally parked vehicles that should not be on the road (e.g. vehicles registered to drivers whose license is under suspension).
3) Identifying legally parked vehicles whose owners have unpaid public debts (e.g. unpaid traffic fines, child support payments, etc.).
4) Identifying legally parked vehicles that are registered to wanted fugitives.
5) Identifying legally parked vehicles whose owners (convicted felons) may be violating their parole conditions by being parked at a particular location at a particular time.
6) Identifying legally parked vehicles having outstanding liens on them that have resulted in judgements for repossession.
Embodiments of the invention that provide some or all of these additional enforcement functions are similar to the parking control embodiments described in detail above. The only functional modification required to realize these embodiments is to link the system to a database
Typically, the “wanted-list” is updated at the end of each working day when data is downloaded from the patrol vehicle for processing through the Parking Authority's administrative system. However, if there is an urgent requirement to locate the vehicle not already on the wanted-list, that vehicle's unique identifier can be broadcast over a wireless communication link to all parking patrol vehicles. The new wanted vehicle is then immediately added to each patrol vehicle's on-board wanted-list so that the Officers will be automatically alerted if the wanted vehicle is encountered.
Embodiments that support the six enforcement functions listed above can be implemented without the need for geo-referencing hardware
Since parking regulations are not being enforced in pure search mode, the plate-strings or transponders IDs recognized by the system need not be geo-referenced or time-stamped. Patrol vehicles operating in pure search mode simply drive about the municipality identifying all parked vehicles. As each parked vehicle is identified, it is searched for in the linked database's “wanted list”
Embodiment That Insures the Privacy of Citizens
The present invention's ability to identify the whereabouts of individual motorists raises concerns that the information could be used to invade the privacy of citizens. Therefore, in a preferred embodiment, all data collected during the course of enforcing parking regulations is encrypted (shown as step