Name
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-grid-based 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. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1109/TIP.2011.2168414 | IEEE Transactions on Image Processing |
Keywords | Field | DocType |
intensively adaptive markov-chain monte,particle filtering (numerical methods),alternative tracking algorithms,visual tracking,approximation theory,motion tracking,motion uncertainty,adaptive markov-chain monte carlo sampling,samc sampling,monte carlo methods,samc sampling method,abrupt motion,abrupt motion tracking via,carlo sampling,mcmc sampler,target distribution,convergence rate,sampling-based tracking scheme,markov processes,bayesian filtering framework,convergence,stochastic approximation,image sampling,local-trap problem,bayes methods,object tracking,robust tracking,bayesian filter tracking framework,large motion uncertainty,abrupt motion problem,computer vision,intensive adaptation,proposal adaptation scheme,filtering distribution,density-grid-based predictive model,mcmc methods,stochastic approximation monte carlo sampling method,sampling efficiency,particle filters,conventional markov-chain monte carlo methods,markov-chain monte carlo (mcmc),image motion analysis,markov process,monte carlo method,bayesian method,sampling methods,markov chain monte carlo,bayesian methods,particle filter,monte carlo,prediction model | Monte Carlo method,Mathematical optimization,Markov chain Monte Carlo,Computer science,Particle filter,Filter (signal processing),Video tracking,Sampling (statistics),Stochastic approximation,Match moving | Journal |
Volume | Issue | ISSN |
21 | 2 | 1941-0042 |
Citations | PageRank | References |
29 | 1.01 | 19 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xiuzhuang Zhou | 1 | 380 | 20.26 |
| Yao Lu | 2 | 98 | 19.25 |
| Jiwen Lu | 3 | 3105 | 153.88 |
| Jie Zhou | 4 | 2103 | 190.17 |