Tracking multiple vehicles using foreground, background and motion models

摘要

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.