Vehicle prediction and association tool based on license plate recognition
First Claim
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.
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Accused Products
Abstract
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.
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Citations
18 Claims
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1. A method comprising:
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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. - View Dependent Claims (2, 3, 4, 5)
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6. A method comprising:
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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. - View Dependent Claims (7, 8, 9)
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10. An apparatus comprising:
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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. - View Dependent Claims (11, 12, 13, 14, 15, 16, 17, 18)
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Specification