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Robust object tracking with active context learning

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Abstract

This paper proposes a method to deal with long-term robust object tracking in unconstrained environment. The approach exploits both target and background information on the fly automatically. It builds the structural constraint using active context learning to enhance the adaptability for variation of the target and stability of tracking. An optical-flow-based motion region extraction method is integrated into the context learning framework to address the problem of fast target motion or abrupt camera motion. Experimental results on challenging real-world video sequences demonstrate the effectiveness and robustness of our approach. Comparisons with several state-of-the-art methods are provided.

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Acknowledgments

This work was supported by the fundamental research funds for the Central Universities of China (Grant No. 2682014cx024 and SWJTU12BR024).

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Authors and Affiliations

  1. School of Electrical Engineering, Southwest Jiaotong University, Chengdu, Sichuan, 610031, People’s Republic of China

    Wei Quan

  2. State Key Laboratory of Traction Power, Southwest Jiaotong University, Chengdu, Sichuan, 610031, People’s Republic of China

    Yongquan Jiang, Jianjun Zhang & Jim X. Chen

  3. National Centre for Computer Animation, Bournemouth University, Poole, UK

    Jianjun Zhang

  4. Department of Computer Science, George Mason University, Fairfax, VA, 22030, USA

    Jim X. Chen

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  1. Wei Quan
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  2. Yongquan Jiang
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  3. Jianjun Zhang
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  4. Jim X. Chen
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Correspondence to Yongquan Jiang.

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Quan, W., Jiang, Y., Zhang, J. et al. Robust object tracking with active context learning. Vis Comput 31, 1307–1318 (2015). https://doi.org/10.1007/s00371-014-1012-8

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  • DOI: https://doi.org/10.1007/s00371-014-1012-8

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