Object tracking via appearance modeling and sparse representation

  • Authors:

  • Feng Chen;Qing Wang;Song Wang;Weidong Zhang;Wenli Xu

  • Affiliations:

  • Tsinghua National Laboratory for Information Science and Technology, Department of Automation, Tsinghua University, Beijing 100084, China;Tsinghua National Laboratory for Information Science and Technology, Department of Automation, Tsinghua University, Beijing 100084, China;Department of Computer Science and Engineering, University of South Carolina, Columbia, SC 29208, USA;National Institutes of Health, Clinical Center, Bethesda, MD 20892, USA;Tsinghua National Laboratory for Information Science and Technology, Department of Automation, Tsinghua University, Beijing 100084, China

  • Venue:
  • Image and Vision Computing
  • Year:
  • 2011

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Abstract

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.