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

Jingjing Wu; Shiqiang Hu; Yang Wang

doi:10.1117/1.3518084

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.

Citation Download Citation

Jingjing Wu, Jingjing Wu, Shiqiang Hu, Shiqiang Hu, Yang Wang, Yang Wang, } "Probability-hypothesis-density filter for multitarget visual tracking with trajectory recognition," Optical Engineering 49(12), 129701 (1 December 2010). https://doi.org/10.1117/1.3518084 . Submission:

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