Location Classification Based on License Plate Recognition Information

• US 20140254879A1
• Filed: 03/08/2014
• Published: 09/11/2014
• Est. Priority Date: 03/08/2013
• Status: Abandoned Application

• Current Assignee: VaaS International Holdings, Inc. (Motorola Solutions, Inc.)
• Original Assignee: VaaS International Holdings, Inc. (Motorola Solutions, Inc.)
• Inventors: Smith, Shawn B.
• Application Number:
  US14/201,830
• Publication Number:
  US 20140254879A1
• Time in Patent Office: Days
• US Class Current: 382/105
• CPC Class Codes: G06V 20/62 Text, e.g. of license plates, overlay texts or captions on TV images
• CPC Class Codes: G06V 20/625 License plates
• CPC Class Codes: G08G 1/017 identifying vehicles G08G1/015, G08G1/054 take precedence
• CPC Class Codes: G08G 1/20 Monitoring the location of vehicles belonging to a group, e.g. fleet of vehicles, countable or determined number of vehicles

Abstract

Methods and systems for Methods and systems for classifying the locations of a vehicle of interest based on License Plate Recognition (LPR) instances are described herein. Locations associated with LPR instances matching a particular license plate number are classified based on LPR information gathered within search zones around each location. Clusters of one or more LPR instances associated with a target license plate number are identified. A search zone is defined around a cluster of one or more LPR instances associated with a target license plate number. LPR instances associated with other license plate numbers within the search zone are received from an LPR server, and a location associated with the search zone is classified based on LPR information gathered within the search zone. In some examples, the location classification is based on LPR activity matching a target license plate number, general LPR activity within the search zone, or both.

Claims

1. A method comprising:

receiving a plurality of LPR instances within a search zone around a cluster of one or more LPR instances associated with a target license plate number, wherein the plurality of LPR instances includes LPR instances associated with a plurality of different license plate numbers;

determining one or more LPR metrics based on the plurality of LPR instances;

classifying a location associated with the cluster of one or more LPR instances based at least in part on the one or more LPR metrics; and

storing an indication of the classification of the location.

2. The method of claim 1, further comprising:

determining the cluster of one or more LPR instances associated with the target license plate number when a number of LPR instances associated with the target license plate number within a population area exceeds a predetermined threshold value.

3. The method of claim 1, further comprising:

determining the cluster of one or more LPR instances associated with the target license plate number as a population of LPR instances associated with the target license plate number having a spatial density greater than a predetermined value.

4. The method of claim 1, further comprising:

determining the search zone around the cluster of one or more LPR instances associated with a target license plate number.

5. The method of claim 4, wherein the search zone is a predetermined shape centered on a centroid of a spatial distribution of the cluster of LPR instances associated with the target license plate number.

6. The method of claim 4, wherein the determining the search zone comprises,

overlaying the cluster of one or more LPR instances associated with the target license plate number on a map, and

determining the search zone based at least in part on one or more features of the map.

7. The method of claim 1, wherein the classifying the location associated with the cluster of one or more LPR instances involves a ratio of LPR instances captured during daytime and LPR instances captured during nighttime within the search zone.

8. The method of claim 1, wherein the classifying the location associated with the cluster of one or more LPR instances involves a percentage of license plate numbers scanned at least two times within the search zone.

9. The method of claim 1, further comprising:

transmitting a first LPR information query to a LPR database, the LPR information query including an indication of a vehicle license plate number;

receiving an address associated with at least one License Plate Recognition (LPR) instance that matches the license plate number in response to the LPR information query, the at least one LPR instance having been previously identified by a LPR system; and

transmitting a second LPR information query to the LPR database, the LPR information query including the search zone around the cluster of one or more LPR instances associated with the target license plate number.

10. An apparatus comprising:

a processor; and

a memory storing an amount of program code that, when executed, causes the apparatus to

receive a plurality of LPR instances within a search zone around a cluster of one or more LPR instances associated with a target license plate number, wherein the plurality of LPR instances includes LPR instances associated with a plurality of different license plate numbers;

determine a plurality of LPR metrics based on the plurality of LPR instances;

classify a location associated with the cluster of one or more LPR instances based at least in part on the plurality of LPR instances; and

store an indication of the classification of the location.

11. The apparatus of claim 10, the memory also storing an amount of program code that, when executed, causes the apparatus to:

determine the cluster of one or more LPR instances associated with the target license plate number as a number of LPR instances associated with the same address, wherein the number exceeds a predetermined threshold value.

12. The apparatus of claim 10, the memory also storing an amount of program code that, when executed, causes the apparatus to:

determine the cluster of one or more LPR instances associated with the target license plate number as a population of LPR instances associated with the target license plate number having a spatial density greater than a predetermined value.

13. The apparatus of claim 10, the memory also storing an amount of program code that, when executed, causes the apparatus to:

determine the search zone around the cluster of one or more LPR instances associated with a target license plate number.

14. The apparatus of claim 13, wherein the search zone is a predetermined shape centered on a centroid of a spatial distribution of the cluster of LPR instances associated with the target license plate number.

15. The apparatus of claim 13, wherein the determining the search zone involves overlaying the cluster of one or more LPR instances associated with the target license plate number on a map, and determining the search zone based at least in part on one or more features of the map.

16. The apparatus of claim 10, wherein the classifying the location associated with the cluster of one or more LPR instances involves a ratio of LPR instances captured during daytime and LPR instances captured during nighttime within the search zone.

17. The apparatus of claim 10, wherein the classifying the location associated with the cluster of one or more LPR instances involves a percentage of license plate numbers scanned at least two times within the search zone.

18. The apparatus of claim 10, the memory also storing an amount of program code that, when executed, causes the apparatus to:

transmit a first LPR information query to a LPR database, the LPR information query including an indication of a vehicle license plate number;

receive an address associated with at least one License Plate Recognition (LPR) instance that matches the license plate number in response to the LPR information query, the at least one LPR instance having been previously identified by a LPR system; and

transmit a second LPR information query to the LPR database, the LPR information query including the search zone around the cluster of one or more LPR instances associated with the target license plate number.

19. A non-transitory, computer-readable medium, comprising:

code for causing a computer to receive a plurality of LPR instances within a search zone around a cluster of one or more LPR instances associated with a target license plate number, wherein the plurality of LPR instances includes LPR instances associated with a plurality of different license plate numbers;

code for causing the computer to determine one or more LPR metrics based on the plurality of LPR instances;

code for causing the computer to classify a location associated with the cluster of one or more LPR instances based at least in part on the one or more LPR metrics; and

code for causing the computer to store an indication of the classification of the location.

20. The non-transitory, computer-readable medium of claim 19, further comprising:

code for causing the computer to determine the cluster of one or more LPR instances associated with the target license plate number when a number of LPR instances associated with the target license plate number within a population area exceeds a predetermined threshold value.