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Object tracking based on support vector dictionary learning

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Abstract

Dictionary learning is widely used to track targets in video sequences. However, a target can be lost during the tracking because of rotation, motion, background clutter, and so on. A dictionary learning method has recently been developed to reduce the chances of missing the target. We developed a new approach using support vector dictionary learning with histograms of sparse codes for a particle filter framework. The representation with support vector can help balance the residual between the candidate and the target. The experiments conducted on challenging sequences demonstrate that the proposed method outperforms seven state-of-the-art algorithms in terms of the overlap rate, center error, and accuracy.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China under Grants 61563036, 61501173, and 61461032 and Natural Science Foundation of Jiangxi Province under Grant No. 20161BAB212037.

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Authors and Affiliations

  1. College of Computer and Information, Hohai University, Nanjing, 210098, China

    Li Lv, Zhe Chen, Zhen Zhang & Lizhong Xu

  2. School of Information Engineering, Nanchang Institute of Technology, Nanchang, 330099, China

    Li Lv & Tanghuai Fan

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  1. Li Lv
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  2. Zhe Chen
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  3. Zhen Zhang
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  4. Tanghuai Fan
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  5. Lizhong Xu
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Correspondence to Lizhong Xu.

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Lv, L., Chen, Z., Zhang, Z. et al. Object tracking based on support vector dictionary learning. SIViP 12, 1189–1196 (2018). https://doi.org/10.1007/s11760-018-1270-4

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