Vehicle prediction and association tool based on license plate recognition
LPR instances around physical locations and license plate numbers associated with a person of interest are analyzed to predict the relative likelihood of locating the person of interest at a particular location. An LPR information query includes an indication of a physical location and a license plate number associated with a person of interest. The relative likelihood of locating a person of interest at a particular location at a future point in time is determined based on the LPR instances received. In one example, the relative likelihood of locating the person of interest is based on the relative value of clusters of LPR instances around physical locations associated with the person of interest. Additional license plate numbers are associated with a person of interest based on their appearance within a search zone and time window of LPR instances of a license plate number already associated with a person of interest.
1. A method comprising:
receiving an indication of a plurality of license plate recognition (LPR) instances within a search area of a physical location associated with a person of interest;
determining a plurality of clusters of the plurality of license plate recognition (LPR) instances associated with the physical location, wherein the determining of each of the clusters of the plurality of LPR instances involves:
determining a time difference between successive LPR instances; and
determining if the time difference between successive LPR instances is less than a predetermined threshold; and
determining at least one attribute of each of the plurality of clusters of LPR instances; and
determining the relative likelihood of locating the person of interest at the physical location based on the at least one attribute.
2. The method of claim 1 , further comprising:
receiving a license plate recognition (LPR) information query including an indication of the physical location and a target license plate number associated with the person of interest; and
determining the plurality of license plate recognition (LPR) instances within the search area of the physical location in response to receiving the LPR information query.
3. The method of claim 2 ,
wherein the determining that least one attribute of each of the plurality of clusters is based on at least one search metric.
4. 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 an LPR system.
5. The method of claim 2 , wherein a license plate number associated with each of the plurality of LPR instances matches the target license plate number associated with the person of interest.
6. A method comprising:
receiving an indication of a physical location and a license plate number associated with a person of interest;
determining a plurality of clusters of license plate recognition (LPR) instances associated with the physical location and the license plate number, wherein the determining of each of the clusters of LPR instances involves:
determining a time difference between successive LPR instances; and
determining if the time difference between successive LPR instances is less than a predetermined threshold; and
determining a relative likelihood of locating the person of interest at a particular location at a future point in time based on at least one attribute of the clusters of LPR instances associated with the physical location and the license plate number.
7. The method of claim 6 , wherein the determining the relative likelihood of locating the person of interest involves:
determining the at least one attribute of each of the clusters based on at least one search metric.
8. The method of claim 6 , wherein the determining of the relative likelihood of locating a person of interest at the physical location involves scaling a value of a cluster based on an amount of time since the last LPR site visit to the physical location.
9. The method of claim 6 , wherein the determining of the relative likelihood of locating a person of interest at the physical location involves scaling a value of a cluster based on a number of LPR site visits to the physical location.
10. An apparatus comprising:
a processor; and
a memory storing an amount of program code that, when executed causes the processor to
receive an indication of a plurality of license plate recognition (LPR) instances within a search area of a physical location associated with a person of interest;
determine a plurality of clusters of the plurality of license plate recognition (LPR) instances associated with the physical location, wherein the determining of each of the clusters of the plurality of LPR instances involves:
determining a time difference between successive LPR instances; and
determining if the time difference between successive LPR instances is less than a predetermined threshold; and
determine at least one attribute of each of the plurality of clusters of LPR instances; and
determine the relative likelihood of locating the person of interest at the physical location based on the at least one attribute.
11. The apparatus of claim 10 , the memory also storing an amount of program code that, when executed causes the processor to:
receive a license plate recognition (LPR) information query including an indication of the physical location and a target license plate number associated with the person of interest; and
determine the plurality of license plate recognition (LPR) instances within the search area of the physical location in response to receiving the LPR information query.
12. The apparatus of claim 11 ,
wherein the determining the at least one attribute of each of the plurality of clusters is based on at least one search metric.
13. The apparatus of claim 10 , 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 an LPR system.
14. The apparatus of claim 11 , wherein a license plate number associated with each of the plurality of LPR instances matches the target license plate number associated with the person of interest.
15. The apparatus of claim 10 , wherein the determining of the relative likelihood of locating a person of interest at the physical location involves scaling a value of a cluster based on an amount of time since the last LPR site visit to the physical location.
16. The apparatus of claim 10 , wherein the determining of the relative likelihood of locating a person of interest at the physical location involves scaling a value of a cluster based on a number of LPR site visits to the physical location.
17. The apparatus of claim 10 , the memory also storing an amount of program code that, when executed causes the processor to:
determine a first license plate number within a first search zone and a first time window around a first LPR instance of a target license plate number;
determine a second license plate number within a second search zone and a second time window around a second LPR instance of the target license plate number; and
associate the first license plate number and the target license plate number if the first license plate number matches the second license plate number.
18. The apparatus of claim 17 , wherein the first LPR instance comprises an indication of a first physical location of the target license plate number and an indication of a first time when an image of the target license plate number was captured, and wherein the second LPR instance comprises an indication of a second physical location of the target license plate number and an indication of a second time when an image of the target license plate number was captured.