Locating persons of interest based on license plate recognition information

• US 8,768,009 B1
• Filed: 07/26/2012
• Issued: 07/01/2014
• Est. Priority Date: 07/26/2011
• Status: Active Grant

• Current Assignee: VaaS International Holdings, Inc. (Motorola Solutions, Inc.)
• Original Assignee: Digital Recognition Network Incorporated (Motorola Solutions, Inc.), Vigilant Solutions Incorporated
• Inventors: Smith, Shawn B.
• Primary Examiner(s): AZARIAN, SEYED H
• Application Number:
  US13/559,469
• Time in Patent Office: 705 Days
• Field of Search: 382/100, 382103-107, 382/155, 382/162, 382/168, 382/173, 382/181, 382/193, 382/209, 382/219, 382/224, 382/232, 382/254, 382/274, 382/276, 382287-291, 382/305, 382/312, 340/937, 701/1
• US Class Current: 382/105
• CPC Class Codes: G06F 16/29 Geographical information databases
• CPC Class Codes: G06F 18/2113 by ranking or filtering the set of features, e.g. using a measure of variance or of feature cross-correlation
• CPC Class Codes: G06F 18/22 Matching criteria, e.g. proximity measures
• CPC Class Codes: G06F 18/23 Clustering techniques
• CPC Class Codes: G06F 18/232 Non-hierarchical techniques
• CPC Class Codes: G06T 2207/30232 Surveillance
• CPC Class Codes: G06T 2207/30252 Vehicle exterior; Vicinity of vehicle
• CPC Class Codes: G06T 7/70 Determining position or orientation of objects or cameras camera calibration G06T7/80
• CPC Class Codes: G06V 10/768 using context analysis, e.g. recognition aided by known co-occurring patterns
• CPC Class Codes: G06V 20/625 License plates
• CPC Class Codes: G08G 1/0175 by photographing vehicles, e.g. when violating traffic rules

Abstract

Possible locations of a person of interest are ranked based on LPR instances captured around physical locations and license plate numbers associated with the person of interest. An LPR instance includes an indication of a vehicle license plate number, a physical location, and a time when the LPR instance was captured by a LPR system. A possible location of the person of interest may be a location of an LPR instance that matches the license plate number or an address location associated with the person of interest. The ranking may be based on the number of LPR visits to each location, the number of license plate number matches at each location, or an attribute of a cluster of LPR instances. In some examples, an electronic message is rapidly communicated to an entity if a target license plate number is found at a highly ranked location.

Claims

1. A method comprising:

receiving an indication of a license plate number of a vehicle associated with a person of interest;

determining a plurality of LPR instances recognized within a search zone around any of a plurality of possible locations of the person of interest by one or more processors, wherein a possible location of the person of interest includes any of a location of an LPR instance that matches the license plate number and an address location associated with the person of interest;

determining a distance between a location of each of a plurality of LPR instances matching a license plate number associated with a person of interest and each address associated with the person of interest;

determining an average distance to the plurality of LPR instances for each address location; and

ranking the plurality of possible locations of the person of interest based at least in part on a plurality of License Plate Recognition (LPR) instances that match the license plate number, wherein the ranking of the plurality of possible locations of the person of interest is based at least in part on the determined average distances for each address location.

2. The method of claim 1, further comprising:

transmitting an indication of the license plate number to a LPR server; and

receiving a plurality of LPR instances that match the license plate number.

3. The method of claim 2, wherein an LPR instance of the plurality of LPR instances includes an indication of a vehicle license plate number, an indication of a physical location, and an indication of a time when the LPR instance was captured by a LPR system.

4. The method of claim 1, further comprising:

determining a number of LPR visits associated with each possible location of the person of interest, and wherein the ranking of the plurality of possible locations of the person of interest is based at least in part on the determined number of LPR visits to each location.

5. The method of claim 1, further comprising:

determining a number of LPR instances that match the license plate number associated with a person of interest that are recognized within a first search zone around each of the possible locations of the person of interest,

wherein the ranking of the plurality of possible locations is based at least in part on the determined number of LPR instances that match the license plate number associated with the person of interest within the first search zone.

6. The method of claim 5, further comprising:

determining a number of LPR instances that match a license plate number associated with a person of interest that are recognized within a second search zone around each of the possible locations of the person of interest, wherein the ranking of the plurality of possible locations is based at least in part on a difference between the determined number of LPR instances that match the license plate number associated with the person of interest in the first search zone and the second search zone.

7. The method of claim 1, further comprising:

determining a plurality of clusters of recognized LPR instances based on a visiting time period that an LPR unit collected LPR instances within the search zone.

8. The method of claim 7, further comprising:

determining at least one attribute of each of the clusters, wherein the ranking of the plurality of possible locations of the person of interest is based at least in part on the at least one attribute of each of the clusters of License Plate Recognition (LPR) instances.

9. The method of claim 8, wherein the determining of the at least one attribute involves determining a number of the LPR instances recognized in each of a plurality of time periods of a day based on whether each LPR instance was scanned during each of the time periods, and wherein the ranking of the possible locations is based at least in part on the number of LPR instances scanned in each of the time periods.

10. The method of claim 9, further comprising:

identifying a location type of at least one of the possible locations based at least in part on the number of LPR instances scanned in each of the time periods.

11. The method of claim 1, further comprising:

determining a monetary value associated with a property at the address location, and wherein the ranking of the possible locations is based at least in part on the monetary value of the property at the address location.

12. The method of claim 1, further comprising

determining whether the person of interest is subject to a live alert;

and determining whether a relative likelihood of locating the person of interest at the location is greater than a predetermined threshold; and

electronically communicating an indication of a location of the license plate number of the vehicle associated with the person of interest based on the ranking of the plurality of possible locations of the person of interest if the person of interest is subject to a live alert.

13. A method comprising:

receiving a plurality of LPR instances that include a plurality of license plate numbers within a search zone around a location;

ranking the plurality of license plate numbers based at least in part on an attribute of a vehicle associated with each of the plurality of license plate numbers by one or more processors, wherein the ranking involves;

determining a vehicle identification number (VIN) associated with each license plate number of the plurality of license plate numbers;

determining an estimated monetary value associated with each determined VIN;

determining an average estimated monetary value based on the determined estimated monetary values; and

identifying a license plate number associated with a vehicle with an estimated monetary value that differs from the average estimated monetary value by more than a threshold value.

14. An apparatus comprising:

a processor; and

a non-transitory computer readable medium storing an amount of program code that, when executed, causes the apparatus to

receive an indication of a license plate number of a vehicle associated with a person of interest;

determine a plurality of LPR instances recognized within a search zone around any of a plurality of possible locations of the person of interest, wherein a possible location of the person of interest includes any of a location of an LPR instance that matches the license plate number and an address location associated with the person of interest;

determine a distance between a location of each of a plurality of LPR instances matching a license plate number associated with a person of interest and each address associated with the person of interest;

determine an average distance to the plurality of LPR instances for each address location; and

rank a plurality of possible locations of the person of interest based at least in part on a plurality of License Plate Recognition (LPR) instances that match the license plate number, wherein the ranking of the plurality of possible locations of the person of interest is based at least in part on the determined average distances for each address location.

15. The apparatus of claim 14, the non-transitory computer readable medium also storing an amount of program code that, when executed, causes the apparatus to transmit an indication of the license plate number to a LPR server;

and

receive a plurality of LPR instances that match the license plate number.

16. The apparatus of claim 15, wherein an LPR instance of the plurality of LPR instances includes an indication of a vehicle license plate number, an indication of a physical location, and an indication of a time when the LPR instance was captured by a LPR system.

17. The apparatus of claim 14, the non-transitory computer readable medium also storing an amount of program code that, when executed,

causes the apparatus to

determine a number of LPR visits associated with each possible location of the person of interest, and wherein the ranking of the plurality of possible locations of the person of interest is based at least in part on the determined number of LPR visits to each location.

18. The apparatus of claim 14, the non-transitory computer readable medium also storing an amount of program code that, when executed, causes the apparatus to determine a number of LPR instances that match the license plate number associated with a person of interest that are recognized within a first search zone around each of the possible locations of the person of interest, wherein the ranking of the plurality of possible locations is based at least in part on the determined number of LPR instances that match the license plate number associated with the person of interest within the first search zone.

19. The apparatus of claim 18, the non-transitory computer readable medium also storing an amount of program code that, when executed, causes the apparatus to determine a number of LPR instances that match a license plate number associated with a person of interest that are recognized within a second search zone around each of the possible locations of the person of interest, wherein the ranking of the plurality of possible locations is based at least in part on a difference between the determined number of LPR instances that match the license plate number associated with the person of interest in the first search zone and the second search zone.

20. The apparatus of claim 14, the non-transitory computer readable medium also storing an amount of program code that, when executed, causes the apparatus to

determine a plurality of clusters of recognized LPR instances based on a visiting time period that an LPR unit collected LPR instances within the search zone.

21. The apparatus of claim 20, the non-transitory computer readable medium also storing an amount of program code that, when executed, causes the apparatus to determine at least one attribute of each of the clusters, wherein the ranking of the plurality of possible locations of the person of interest is based at least in part on the at least one attribute of each of the clusters of License Plate Recognition (LPR) instances.

22. The apparatus of claim 21, wherein the attribute includes a number of the LPR instances recognized in each of a plurality of time periods of a day based on whether each LPR instance was scanned during each of the time periods, and wherein the ranking of the possible locations is based at least in part on the number of LPR instances scanned in each of the time periods.

23. The apparatus of claim 14, the non-transitory computer readable medium also storing an amount of program code that, when executed, causes the apparatus to

determine a monetary value associated with a property at the address location, and wherein the ranking of the possible locations is based at least in part on the monetary value of the property at the address location.

24. The apparatus of claim 14, the non-transitory computer readable medium also storing an amount of program code that, when executed, causes the apparatus to

determine whether the person of interest is subject to a live alert;

and

electronically communicate an indication of a location of the license plate number of the vehicle associated with the person of interest based on the ranking of the plurality of possible locations of the person of interest if the person of interest is subject to a live alert.