CONDENSATION—Conditional Density Propagation forVisual Tracking
International Journal of Computer Vision
MCMC-Based Particle Filtering for Tracking a Variable Number of Interacting Targets
IEEE Transactions on Pattern Analysis and Machine Intelligence
ACM Computing Surveys (CSUR)
Tracking of Abrupt Motion Using Wang-Landau Monte Carlo Estimation
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
Tracking nonstationary visual appearances by data-driven adaptation
IEEE Transactions on Image Processing
Rao-Blackwellised particle filter for colour-based tracking
Pattern Recognition Letters
A tutorial on particle filters for online nonlinear/non-GaussianBayesian tracking
IEEE Transactions on Signal Processing
Abrupt Motion Tracking Via Intensively Adaptive Markov-Chain Monte Carlo Sampling
IEEE Transactions on Image Processing
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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.