13-11-2012, 02:29 PM
Abrupt Motion Tracking Via Intensively Adaptive Markov-Chain Monte Carlo Sampling
ABSTRACT
The robust tracking of abrupt motion is a challenging task in computer vision due to its large motion
uncertainty. While various particle filters and
conventional Markov-chain Monte Carlo (MCMC)
methods have been proposed for visual tracking, these
methods often suffer from the well-known local-trap
problem or from poor convergence rate. In this paper, we
propose a novel sampling-based tracking scheme for the
abrupt motion problem in the Bayesian filtering
framework. To effectively handle the local-trap problem,
we first introduce the stochastic approximation Monte
Carlo (SAMC) sampling method into the Bayesian filter
tracking framework, in which the filtering distribution is
adaptively estimated as the sampling proceeds, and thus,
a good approximation to the target distribution is
achieved. In addition, we propose a new MCMC sampler
with intensive adaptation to further improve the
sampling efficiency, which combines a density-gridbased
predictive model with the SAMC sampling, to
give a proposal adaptation scheme. The proposed method
is effective and computationally efficient in addressing
the abrupt motion problem. We compare our approach
with several alternative tracking algorithms, and
extensive experimental results are presented to
demonstrate the effectiveness and the efficiency of the
proposed method in dealing with various types of abrupt
motions.