A real-time multiple target tracking algorithm using merged probabilistic data association technique and smoothing particle filter

In this paper, we present a tracking system that combines the merged probabilistic data association (MPDA) technique together with the smoothing particle filter to track multiple targets. The MPDA approach combines the probabilistic nearest-neighbor filter (PNNF) together with the probabilistic data association (PDA) approach, in the data association step, to track multiple targets in dense clutter environment. Due to the high uncertainty when applying a particle filter to track a maneuverable target, the smoothing particle filter is used. Results show that combining MPDA together with smoothing particle filter can achieve a robust and real-time tracking system for tracking multiple targets even in dense clutter environment.

[1]  Samuel S. Blackman,et al.  Multiple-Target Tracking with Radar Applications , 1986 .

[2]  Wolfram Burgard,et al.  Tracking multiple moving targets with a mobile robot using particle filters and statistical data association , 2001, Proceedings 2001 ICRA. IEEE International Conference on Robotics and Automation (Cat. No.01CH37164).

[3]  H. O. Hartley,et al.  Machine-Generation of Order Statistics for Monte Carlo Computations , 1972 .

[4]  Wael Badawy,et al.  Comparison between smoothing and auxiliary particle filter in tracking a maneuverable target in a multiple sensor network , 2005 .

[5]  Robin J. Evans,et al.  Integrated probabilistic data association , 1994, IEEE Trans. Autom. Control..

[6]  Samuel S. Blackman,et al.  Design and Analysis of Modern Tracking Systems , 1999 .

[7]  Robert J. Fitzgerald,et al.  Development of Practical PDA Logic for Multitarget Tracking by Microprocessor , 1986, 1986 American Control Conference.

[8]  Roy L. Streit,et al.  Maximum likelihood method for probabilistic multihypothesis tracking , 1994, Defense, Security, and Sensing.

[9]  Yi Shen,et al.  A new smoothing particle filter for tracking a maneuvering target , 2003, Proceedings of the 2003 International Conference on Machine Learning and Cybernetics (IEEE Cat. No.03EX693).

[10]  M. Farooq,et al.  A comparison of data association techniques for target tracking in clutter , 2002, Proceedings of the Fifth International Conference on Information Fusion. FUSION 2002. (IEEE Cat.No.02EX5997).

[11]  Quan Pan,et al.  Multitarget tracking using dominant probability data association , 1994, Proceedings of 1994 American Control Conference - ACC '94.

[12]  X. R. Li,et al.  The PDF of nearest neighbor measurement and a probabilistic nearest neighbor filter for tracking in clutter , 1993, Proceedings of 32nd IEEE Conference on Decision and Control.

[13]  N. Bergman,et al.  Auxiliary particle filters for tracking a maneuvering target , 2000, Proceedings of the 39th IEEE Conference on Decision and Control (Cat. No.00CH37187).

[14]  Yaakov Bar-Shalom,et al.  Multi-target tracking using joint probabilistic data association , 1980, 1980 19th IEEE Conference on Decision and Control including the Symposium on Adaptive Processes.

[15]  J. A. Roecker,et al.  Suboptimal joint probabilistic data association , 1993 .

[16]  C. Hue,et al.  A particle filter to track multiple objects , 2001, Proceedings 2001 IEEE Workshop on Multi-Object Tracking.

[17]  Y. Bar-Shalom,et al.  Tracking in a cluttered environment with probabilistic data association , 1975, Autom..

[18]  Jun S. Liu,et al.  Metropolized independent sampling with comparisons to rejection sampling and importance sampling , 1996, Stat. Comput..