Abstract: Methods and systems for estimating the speed of passing vehicles based on License Plate Recognition (LPR) image information are described herein. The distance traversed by a vehicle between image frames is estimated based on a difference between a pixel location of a tracking point on the vehicle in a first image and a second pixel location of the tracking point in second image of the vehicle. In some embodiments, the displacement identified in the images is converted to a projected displacement on the roadway surface based on the mapping between pixel locations within the field of view of the image sensor and locations on the surface of the roadway. The projected displacement is corrected by accounting for the difference in height of the camera module and tracking point over the roadway surface. An estimate of vehicle speed is calculated from the corrected displacement and the elapsed time between image frames.

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.

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.

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.