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Robust visual tracking based on structured sparse representation model

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Abstract

Sparse representation has been one of the most influential frameworks for visual tracking. However, most tracking methods based on sparse representation only consider the holistic representation and lack local information, which may lead to fail when there is similar object or occlusion in the scene. In this paper, we present a novel robust visual tracking algorithm based on structured sparse representation model. This model includes one fixed template, nine variational templates and the background templates, which are selectively updated to adapt to the appearance change of the target. And the update scheme is developed by exploiting the strength of the incremental PCA learning and sparse representation. By incorporating the block-division feature into sparse representation framework, it can capture the intrinsic structured distribution of sparse coefficients effectively and reduce the influence of the occluded target template. In addition, we propose a sparsity-based discriminative classifier, which employ the distinction of reconstruction error between the foreground and the background to improve discrimination performance for object tracking. Both qualitative and quantitative evaluations on benchmark challenging sequences demonstrate that the proposed tracking algorithm performs favorably against several state-of-the-art tracking methods.

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Acknowledgment

This work was supported by Chinese Forestry Industry Research Special Funds for Public Welfare (Grant No.201104090). The Specialized Research Fund for the Doctoral Program of Higher Education (SRFDP)(20120161110014), New Century Excellent Talents in University (NCET-11-0134), National Natural Science Foundation of China (61072122), and Key Project of Hunan Provincial Natural Science Foundation (11JJ2053).

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Authors and Affiliations

  1. School of Information Science and Engineering, Hunan University, Changsha, 410082, China

    Hanling Zhang, Fei Tao & Gaobo Yang

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  1. Hanling Zhang
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  2. Fei Tao
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  3. Gaobo Yang
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Correspondence to Fei Tao.

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Zhang, H., Tao, F. & Yang, G. Robust visual tracking based on structured sparse representation model. Multimed Tools Appl 74, 1021–1043 (2015). https://doi.org/10.1007/s11042-013-1709-0

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