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Robust particle tracker via Markov Chain Monte Carlo posterior sampling

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Abstract

Particle Filter has grown to be a standard framework for visual tracking. This paper proposes a robust particle tracker based on Markov Chain Monte Carlo method, aiming at solving the thorny problems in visual tracking induced by object appearance changes, occlusions, background clutter, and abrupt motions. In this algorithm, we derive the posterior probability density function based on second order Markov assumption. The posterior probability density is the joint density of the previous two states. Additionally, a Markov Chain with certain length is used to approximate the posterior density to avoid the drawbacks of traditional importance sampling based algorithm, which consequently improves the searching ability of the proposed tracker. We compare our approach with several alternative tracking algorithms, and the experimental results demonstrate that our tracker is superior to others in dealing with various types of challenging scenarios.

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Acknowledgments

This work was supported by the National Natural Science Foundation of China (No. 60772063, 61073133, 61175053, 60973067, 61175096, 61272369), the Innovation Group Project of China Education Ministry (No. 2011ZD010, Fundamental research fund of DMU (NO. 2012QN030), Foundation of Scientific Planning Project of Dalian City (No. 2011E15SF100), the Natural Science Foundation of Liaoning Education Ministry (No. L2011241, L2010043).

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Authors and Affiliations

  1. School of Information Science and Technology, Dalian Maritime University, Information Building B-106, No.1 Linghai Road, Ganjingzi District, Dalian, Liaoning Province, China, 116026

    Fasheng Wang & Mingyu Lu

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  1. Fasheng Wang
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  2. Mingyu Lu
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Correspondence to Mingyu Lu.

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Wang, F., Lu, M. Robust particle tracker via Markov Chain Monte Carlo posterior sampling. Multimed Tools Appl 72, 573–589 (2014). https://doi.org/10.1007/s11042-013-1379-y

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