Volume 7 Number 1
February 2010
Article Contents
Qi-Cong Wang, Yuan-Hao Gong, Chen-Hui Yang and Cui-Hua Li. Robust Object Tracking under Appearance Change Conditions. International Journal of Automation and Computing, vol. 7, no. 1, pp. 31-38, 2010. doi: 10.1007/s11633-010-0031-9
Cite as: Qi-Cong Wang, Yuan-Hao Gong, Chen-Hui Yang and Cui-Hua Li. Robust Object Tracking under Appearance Change Conditions. International Journal of Automation and Computing, vol. 7, no. 1, pp. 31-38, 2010. doi: 10.1007/s11633-010-0031-9

Robust Object Tracking under Appearance Change Conditions

Author Biography:
  • Corresponding author: Qi-Cong Wang graduated from Nanjing University of Aeronautics and Astronautics,PRC in 1997.
  • Received: 2009-03-12
Fund Project:

supported by National Natural Science Foundation of China (No.40627001);the 985 Innovation Project on Information Technique of Xiamen University (20042008)

通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

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Robust Object Tracking under Appearance Change Conditions

    Corresponding author: Qi-Cong Wang graduated from Nanjing University of Aeronautics and Astronautics,PRC in 1997.
Fund Project:

supported by National Natural Science Foundation of China (No.40627001);the 985 Innovation Project on Information Technique of Xiamen University (20042008)

Abstract: We propose a robust visual tracking framework based on particle filter to deal with the object appearance changes due to varying illumination, pose variantions, and occlusions. We mainly improve the observation model and re-sampling process in a particle filter. We use on-line updating appearance model, affine transformation, and M-estimation to construct an adaptive observation model. On-line updating appearance model can adapt to the changes of illumination partially. Affine transformation-based similarity measurement is introduced to tackle pose variantions, and M-estimation is used to handle the occluded object in computing observation likelihood. To take advantage of the most recent observation and produce a suboptimal Gaussian proposal distribution, we incorporate Kalman filter into a particle filter to enhance the performance of the resampling process. To estimate the posterior probability density properly with lower computational complexity, we only employ a single Kalman filter to propagate Gaussian distribution. Experimental results have demonstrated the effectiveness and robustness of the proposed algorithm by tracking visual objects in the recorded video sequences.

Qi-Cong Wang, Yuan-Hao Gong, Chen-Hui Yang and Cui-Hua Li. Robust Object Tracking under Appearance Change Conditions. International Journal of Automation and Computing, vol. 7, no. 1, pp. 31-38, 2010. doi: 10.1007/s11633-010-0031-9
Citation: Qi-Cong Wang, Yuan-Hao Gong, Chen-Hui Yang and Cui-Hua Li. Robust Object Tracking under Appearance Change Conditions. International Journal of Automation and Computing, vol. 7, no. 1, pp. 31-38, 2010. doi: 10.1007/s11633-010-0031-9
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