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Robust object tracking based on sparse representation and incremental weighted PCA

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Abstract

Object tracking plays a crucial role in many applications of computer vision, but it is still a challenging problem due to the variations of illumination, shape deformation and occlusion. A new robust tracking method based on incremental weighted PCA and sparse representation is proposed. An iterative process consisting of a soft segmentation step and a foreground distribution update step is adpoted to estimate the foreground distribution, cooperating with incremental weighted PCA, we can get the target appearance in terms of the PCA components with less impact of the background in the target templates. In order to make the target appearance model more discriminative, trivial and background templates are both added to the dictionary for sparse representation of the target appearance. Experiments show that the proposed method with some level of background awareness is robust against illumination change, occlusion and appearance variation, and outperforms several latest important tracking methods in terms of tracking performance.

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Acknowledgments

This work is supported by the National Natural Science Foundation of China (Grant No.61171142, 61401163), and the Science and Technology Planning Project of Guangdong Province of China (No. 2011A010801005).

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Authors and Affiliations

  1. School of Electronic and Information Engineering, South China University of Technology, Guangzhou, China

    Xiaofen Xing, Fuhao Qiu, Xiangmin Xu, Chunmei Qing & Yinrong Wu

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  1. Xiaofen Xing
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  2. Fuhao Qiu
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  3. Xiangmin Xu
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  4. Chunmei Qing
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  5. Yinrong Wu
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Correspondence to Xiangmin Xu.

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Xing, X., Qiu, F., Xu, X. et al. Robust object tracking based on sparse representation and incremental weighted PCA. Multimed Tools Appl 76, 2039–2057 (2017). https://doi.org/10.1007/s11042-015-3164-6

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  • DOI: https://doi.org/10.1007/s11042-015-3164-6

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