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Erschienen in:
2013 | OriginalPaper | Buchkapitel
A Robust Particle Tracker via Markov Chain Monte Carlo Posterior Sampling
verfasst von : Fasheng Wang, Mingyu Lu, Liran Shen
Erschienen in: Computer Vision - ACCV 2012 Workshops
Verlag: Springer Berlin Heidelberg
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Particle Filters have 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 change, occlusion, background clutter, and abrupt motion, etc. 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, 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.