Visual object tracking via sample-based Adaptive Sparse Representation (AdaSR)

  • Authors:

  • Zhenjun Han;Jianbin Jiao;Baochang Zhang;Qixiang Ye;Jianzhuang Liu

  • Affiliations:

  • Graduate University of Chinese Academy of Sciences, No. 19A, Yu Quan Road, Shi Jing Shan District, 100049 Beijing, PR China;Graduate University of Chinese Academy of Sciences, No. 19A, Yu Quan Road, Shi Jing Shan District, 100049 Beijing, PR China;Beijing University of Aeronautics and Astronautics, China;Graduate University of Chinese Academy of Sciences, No. 19A, Yu Quan Road, Shi Jing Shan District, 100049 Beijing, PR China;Department of Information Engineering, The Chinese University of Hong Kong, Hong Kong

  • Venue:
  • Pattern Recognition
  • Year:
  • 2011

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Abstract

When appearance variation of object and its background, partial occlusion or deterioration in object images occurs, most existing visual tracking methods tend to fail in tracking the target. To address this problem, this paper proposes a new approach for visual object tracking based on Sample-Based Adaptive Sparse Representation (AdaSR), which ensures that the tracked object is adaptively and compactly expressed with predefined samples. First, the Sample-Based Sparse Representation, which selects a subset of samples as a basis for object representation by exploiting L1-norm minimization, improves the representation adaptation to partial occlusion for tracking. Second, to keep the temporal consistency and adaptation to appearance variation and deterioration in object images during the tracking process, the object's Sample-Based Sparse Representation is adaptively evaluated based on a Kalman filter, obtaining the AdaSR. Finally, the candidate holding the most similar Sample-Based Sparse Representation to the AdaSR of the tracked object will be regarded as the instantaneous tracking result. In addition, we can easily extend the AdaSR for multi-object tracking by integrating the sample set of each tracked object (named Common Sample-Based Adaptive Sparse Representation Analysis (AdaSRA)). AdaSRA fully analyses Adaptive Sparse Representation similarity for object classification. Our experiments on public datasets show state-of-the-art results, which are better than those of several representative tracking methods.