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Multimedia Systems

, Volume 22, Issue 2, pp 255–269 | Cite as

Robust tracking with adaptive appearance learning and occlusion detection

  • Jianwei Ding
  • Yunqi Tang
  • Huawei Tian
  • Wei Liu
  • Yongzhen Huang
Regular Paper

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.

Keywords

Object tracking Manifold Occlusion detection Graph cuts 

Notes

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.

Supplementary material

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Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  • Jianwei Ding
    • 1
  • Yunqi Tang
    • 1
  • Huawei Tian
    • 1
  • Wei Liu
    • 2
  • Yongzhen Huang
    • 3
  1. 1.People’s Public Security University of ChinaBeijingChina
  2. 2.Nanyang Normal UniversityNanyangChina
  3. 3.National Laboratory of Pattern Recognition, Institute of AutomationChinese Academy of SciencesBeijingChina

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