Vehicle parameterization and tracking from traffic videos

The popularity of surveillance cameras used in traffic management systems have produced large quantities of video data that cannot be processed easily by humans. We present a method used on high resolution traffic surveillance videos to track and estimate vehicles´ state when the cameras are mounted on moderate height structures typically less than 10 meters. This tracking method enables a number of applications and does not have the infrastructure requirements of other vehicle tracking methods. The method requires that both the internal and external camera parameters are calibrated and that vehicles move on a ground plane. Each of the vehicles using this tracking process is parameterized as a rectangular cuboid with dimensions (length, width, and height) and state (position and attitude) reflecting that of the vehicle. From a traffic video stream, visible features on the surface of a vehicle are selected and tracked. A particle filter is used to infer the vehicle´s state as the vehicle moves through the camera´s field of view. In this paper, we present the method, as well as results from real and simulated data, which demonstrate robust tracking and state estimation for a variety of vehicle types.