Probability-hypothesis-density filter for multitarget visual tracking with trajectory recognition

Abstract

The probability-hypothesis-density (PHD) filter as a multitarget recursive Bayes filter has generated substantial interest in the visual tracking field due to its ability to handle a time-varying number of 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 a motion detection, dynamic, and measurement equation, as well as visual multitarget tracking algorithm based on Gaussian mixture probability hypothesis density with trajectory computation in detail. Experimental results on a large video surveillance dataset show that the proposed multitarget tracking framework improves the tracker and recognizes tracks when a variable number of targets appear, merge, split, and disappear, even in cluttered scenes.