Tracking Rectangular Targets in Surveillance Videos with the GM-PHD Filter

This paper describes the application of a Gaussian Mixture Probability Hypoth- esis Density (GM-PHD) ??lter for tracking objects in surveillance video. Clark et al. have proposed a point-based GM-PHD ??lter designed for track label consis- tency. However, this cannot be used for track consistency when using rectangles covering an object. The proposed solution modi??es this ??lter to increase tracking performance when objects split and overlap. Results on synthetic data and real data show that the number of false detections is slightly lower using the rectan- gle GM-PHD, for the same error distance. The advantage is that split objects are better handled (10%-20% lower error distance) by the rectangle GM-PHD ??lter. We conclude that the overall performance is slightly better of the proposed rectangle tracker, but improvements in occlusion handling are required.