PHD filter for multi-target visual tracking with trajectory recognition
Jingjing Wu, Shiqiang Hu · 2010
Probability hypothesis density (PHD) filter, as a multi-target recursive Bayes filter, has generated substantial interest in the visual tracking field due to its ability to handle a time-varying number of nonlinear targets. But the target's trajectory cannot be identified within its own framework. To complement the ability of PHD, the auction algorithm is combined to calculate the object trajectories automatically. We present the motion detection, dynamic and measurement equation, as well as visual multi-target tracking algorithm based on Gaussian mixture probability hypothesis density (GM-PHD) in details. Experimental results on a large video surveillance dataset show the proposed multi-target tracking framework improves the tracker and recognizes the tracks when a variable number of targets appear, merge, split and disappear even in cluttered scenes.