Block covariance based l1 tracker with a subtle template dictionary

  • Authors:

  • Xiaoqin Zhang;Wei Li;Weiming Hu;Haibin Ling;Steve Maybank

  • Affiliations:

  • Institute of Intelligent System and Decision, Wenzhou University, Zhejiang, China;National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China;National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China;Department of Computer and Information Science, Temple University, Philadelphia, USA;Department of Computer Science and Information Systems, Birkbeck College, London, UK

  • Venue:
  • Pattern Recognition
  • Year:
  • 2013

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Abstract

Sparse representation is one of the most influential frameworks for visual tracking. However, when applying this framework to the real-world tracking applications, there are still many challenges such as appearance variations and background noise. In this paper, we propose a new l"1-regularized sparse representation based tracking algorithm. The contributions of our work are: (1) A block-division based covariance feature is incorporated into the sparse representation framework. This feature has two advantages-(a) the feature is more discriminative than the original image patch and (b) the block information is robust for occlusion reasoning. (2) A subtle template dictionary is constructed including a fixed template, a stable template and other variational templates; and these templates are selectively updated to capture the appearance variations and prevent the model from drifting. (3) The sparse representation framework is extended to multi-object tracking, where the multi-object tracking task can be easily decentralized to a set of individual trackers. Experimental results demonstrate that, compared with several state-of-the-art tracking algorithms, the proposed algorithm is more robust and effective.