¹College of Information Science and Engineering, Yanshan University, Qinhuangdao, China
²Department of Computer science, Shijiazhuang University, Shijiazhuang, China

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Abstract—Particle filter is a probability estimation method based on Bayesian framework and it has unique advantage to describe the target tracking non-linear and non-Gaussian. In this paper, Firstly, analyses the particle degeneracy and sample impoverishment in particle filter multi-target tracking algorithm, and secondly, it applies Markov Chain Monte Carlo (MCMC) method to improve re-sampling process and enhance performance of particle filter algorithm. Finally, the performance of the proposed method is certificated by experiment that tracking multiple targets of similar appearance and complex motion. The results show the efficacy of the proposed method in multi-target tracking.

Index Terms—Particle filter; multi-target tracking; sequential important sampling; MCMC.

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Cite: Junying Meng, Jiaomin Liu, Juan Wang, Ming Han, "Target Tracking Based on Optimized Particle Filter Algorithm," Journal of Software vol. 8, no. 5, pp. 1140-1144, 2013.