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Visual tracking by appearance modeling and sparse representation

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3 Author(s)
Qing Wang ; Tsinghua National Laboratory for Information Science and Technology, Department of Automation, Tsinghua University, Beijing, China, 100084 ; Feng Chen ; Wenli Xu

Appearance variation is a big challenge for object tracking. To deal with this problem, we propose a robust tracking method by online appearance modeling and sparse representation. In this method, we use the intensity matrix of image to represent the object, and learn a low dimensional subspace online to model the object appearance variations during tracking. Then applying the recent theory of sparse representation [1], we construct a likelihood function to measure the similarity between an object candidate and the learned appearance model. After that, tracking is led by the Bayesian inference framework, in which a particle filter is utilized to recursively estimate the object state over time. Theoretic analysis and experiments compared with state-of-the-art methods demonstrate the effectiveness of the proposed algorithm.

Published in:

2010 Sixth International Conference on Natural Computation  (Volume:3 )

Date of Conference:

10-12 Aug. 2010