期刊
IMAGE AND VISION COMPUTING
卷 29, 期 11, 页码 787-796出版社
ELSEVIER
DOI: 10.1016/j.imavis.2011.08.006
关键词
Target variation; Online appearance modeling; Sparse representation; Bayesian inference
类别
资金
- National Natural Science Foundation of China [60772050, 61071131]
- No.2 Important National Science and Technology Specific Projects [2009ZX02001]
- United Technologies Research Center (UTRC)
This paper proposes a robust tracking method by the combination of appearance modeling and sparse representation. In this method, the appearance of an object is modeled by multiple linear subspaces. Then within the sparse representation framework, we construct a similarity measure to evaluate the distance between a target candidate and the learned appearance model. Finally, tracking is achieved by Bayesian inference, in which a particle filter is used to estimate the target state sequentially over time. With the tracking result, the learned appearance model will be updated adaptively. The combination of appearance modeling and sparse representation makes our tracking algorithm robust to most of possible target variations due to illumination changes, pose changes, deformations and occlusions. Theoretic analysis and experiments compared with state-of-the-art methods demonstrate the effectivity of the proposed algorithm. (C) 2011 Elsevier B.V. All rights reserved.
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