Locating persons of interest based on license plate recognition information

• US 9,361,546 B1
• Filed: 12/02/2015
• Issued: 06/07/2016
• Est. Priority Date: 07/26/2011
• Status: Active Grant

• Current Assignee: VaaS International Holdings, Inc. (Motorola Solutions, Inc.)
• Original Assignee: VaaS, Inc.
• Inventors: Smith, Shawn B.
• Primary Examiner(s): Azarian, Seyed
• Application Number:
  US14/957,272
• Time in Patent Office: 188 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, 382/321, 340/937, 340/932.2, 340/933, 235/384, 705/13
• US Class Current: 1/1
• 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 onto a computing system including a processor and a non-transitory computer readable medium storing an amount of program code that when executed by the processor is for;

determining a plurality of License Plate Recognition (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;

determining a number of LPR visits associated with each possible location of the person of interest;

determining a time of day associated with each of the number of LPR visits associated with a possible location of the person of interest;

identifying a property type of the possible location based at least in part on the time of day associated with each of the number of LPR visits associated with the possible location and the number of LPR instances scanned in each of the number of LPR visits; and

ranking the plurality of possible locations of the person of interest based at least in part on a plurality of LPR instances that match the license plate number and the number of LPR visits to each location.

2. The method of claim 1, 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.

3. The method of claim 1, wherein an LPR visit is a period of time when an LPR unit approached a particular location, collected LPR information, and subsequently left the particular location.

4. 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.

5. The method of claim 4, 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.

6. The method of claim 1, further comprising:

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; and

determining an average distance to the plurality of LPR instances for each address location, 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.

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 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. A method comprising:

receiving a plurality of LPR instances that include a plurality of license plate numbers within a search zone around a location onto a computing system including a processor and a non-transitory computer readable medium storing an amount of program code that when executed by the processor is for;

determining a time of day associated with each of a number of LPR visits associated with a possible location of the person of interest;

identifying a property type of the possible location based at least in part on the time of day associated with each of the number of LPR visits associated with the possible location and the number of LPR instances scanned in each of the number of LPR visits; and

ranking the possible location of the person of interest based at least in part on a plurality of LPR instances that match a license plate number associated with the person of interest and the number of LPR visits to the location.

11. 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 License Plate Recognition (LPR) instances recognized within a search zone around any of the 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 number of LPR visits associated with each possible location of the person of interest;

determine a time of day associated with each of the number of LPR visits associated with a possible location of the person of interest;

identify a property type of the possible location based at least in part on the time of day associated with each of the number of LPR visits associated with the possible location and the number of LPR instances scanned in each of the number of LPR visits; and

rank the plurality of possible locations of the person of interest based at least in part on a plurality of LPR instances that match the license plate number and the number of LPR visits to each location.

12. The apparatus of claim 11, wherein an LPR visit is a period of time when an LPR unit approached a particular location, collected LPR information, and subsequently left the particular location.

13. The apparatus of claim 11, 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.

14. The apparatus of claim 13, 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.

15. The apparatus of claim 11, the non-transitory computer readable medium also storing an amount of program code that, when executed, causes the apparatus to

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;

and

determine an average distance to the plurality of LPR instances for each address location, 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.

16. The apparatus of claim 11, 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.

17. The apparatus of claim 16, 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.

18. The apparatus of claim 17, 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.