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Robust tracking with adaptive appearance learning and occlusion detection

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Abstract

It is still challenging to design a robust and efficient tracking algorithm in complex scenes. We propose a new object tracking algorithm with adaptive appearance learning and occlusion detection in an efficient self-tuning particle filter framework. The appearance of an object is modeled with a set of weighted and ordered submanifolds, which can guarantee the adaptability when there is fast illumination or pose change. To overcome the occlusion problem, we use the reconstruction error data of the appearance model to extract occlusion region by graph cuts. And the tracking result is improved with feedback of occlusion detection. The motion model is also integrated with adaptability to overcome the abrupt motion problem. To improve the efficiency of particle filter, the number of samples is tuned with respect to the scene conditions. Experimental results demonstrate that our algorithm can achieve great robustness, high accuracy and good efficiency in challenging scenes.

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Acknowledgments

This work is supported by the Fundamental Research Funds for the Central Universities (2014JKF01116), the National High Technology Research and Development Program of China (2013AA014604), National Natural Science Foundation of China (61402484, 61203252), SAMSUNG GRO Program, CCF-Tencent Program and 360 OpenLab Program.

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Authors and Affiliations

  1. People’s Public Security University of China, Beijing, China

    Jianwei Ding, Yunqi Tang & Huawei Tian

  2. Nanyang Normal University, Nanyang, Henan, China

    Wei Liu

  3. National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Yongzhen Huang

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  1. Jianwei Ding
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  2. Yunqi Tang
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  3. Huawei Tian
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  4. Wei Liu
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  5. Yongzhen Huang
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Correspondence to Jianwei Ding.

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Communicated by T. Mei.

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Ding, J., Tang, Y., Tian, H. et al. Robust tracking with adaptive appearance learning and occlusion detection. Multimedia Systems 22, 255–269 (2016). https://doi.org/10.1007/s00530-015-0460-y

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  • DOI: https://doi.org/10.1007/s00530-015-0460-y

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