Markov chain Monte Carlo methods for visual tracking
نویسنده
چکیده
Tracking articulated figures in high dimensional state spaces require tractable methods for inferring posterior distributions of joint locations, angles, and occlusion parameters. Markov chain Monte Carlo (MCMC) methods are efficient sampling procedures for approximating probability distributions. We apply MCMC to the domain of people tracking and investigate a general framework for sample-approximation tracking based on the Particle Filter, MCMC, and simulated annealing. A tutorial discussion of MCMC is provided.
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