Tracking multiple vehicles using foreground, background and motion models

In this paper a vehicle tracking algorithm is presented based on the combination of a novel per-pixel (Gaussian Mixture Based) background model and a set of foreground models of object size, position, velocity, and colour distribution. Each pixel in the scene is 'explained' as either background, belonging to a foreground object, or as noise. A projective ground-plane transform is used within the foreground model to strengthen object size and velocity consistency assumptions. A learned model of typical road travel direction and speed is used to provide a prior estimate of object velocity, which is used to initialise the velocity model for each of the foreground objects. The system runs at near video framerate (>20 fps) on modest hardware and is robust assuming sufficient image resolution is available and vehicle sizes do not greatly exceed the priors on object size used in object initialisation. (C) 2003 Elsevier B.V. All rights reserved.