Sciweavers

Share
CVPR
2010
IEEE

Abrupt motion tracking via adaptive stochastic approximation Monte Carlo sampling

7 years 9 months ago
Abrupt motion tracking via adaptive stochastic approximation Monte Carlo sampling
Robust tracking of abrupt motion is a challenging task in computer vision due to the large motion uncertainty. In this paper, we propose a stochastic approximation Monte Carlo (SAMC) based tracking scheme for abrupt motion problem in Bayesian filtering framework. In our tracking scheme, the particle weight is dynamically estimated by learning the density of states in simulations, and thus the local-trap problem suffered by the conventional MCMC sampling-based methods could be essentially avoided. In addition, we design an adaptive SAMC sampling method to further speed up the sampling process for tracking of abrupt motion. It combines the SAMC sampling and a density grid based statistical predictive model, to give a data-mining mode embedded global sampling scheme. It is computationally efficient and effective in dealing with abrupt motion difficulties. We compare it with alternative tracking methods. Extensive experimental results showed the effectiveness and efficienc...
Xiuzhuang Zhou and Yao Lu
Added 06 Mar 2012
Updated 06 Mar 2012
Type Conference
Year 2010
Where cvpr
Authors Xiuzhuang Zhou and Yao Lu
Comments (0)
books