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Robust object tracking under appearance change conditions

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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.

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Correspondence to Qi-Cong Wang.

Additional information

This work was supported by National Natural Science Foundation of China (No. 40627001) and the 985 Innovation Project on Information Technique of Xiamen University (2004–2008).

Qi-Cong Wang graduated from Nanjing University of Aeronautics and Astronautics, PRC in 1997. He received the M. Sc. degree from Zhejiang University of Technology, PRC in 2004, and the Ph.D. degree from Zhejiang University, PRC in 2007. He is currently an assistant professor in the Department of Computer Science at Xiamen University, PRC.

His research interests include computer vision, image processing, information fusion, and artificial intelligence.

Yuan-Hao Gong received the bachelor degree from Tsinghua University, PRC in 2007. He is currently a master student at Department of Computer Science, Xiamen University, PRC. He is a member of IEEE and ACM.

His research interests include image processing and understanding, computer vision, and pattern recognition.

Chen-Hui Yang received the Ph.D. degree in Zhejiang University, PRC. He is currently a professor in the Department of Computer Science at Xiamen University, PRC.

His research interests include computer visions, image processing and analysis, artificial intelligence in traffic and its application.

Cui-Hua Li received the Ph.D. degree in the Institute of Artificial Intelligence and Robotics from Xi’an Jiaotong University, PRC. He is currently a professor in the Department of Computer Science at Xiamen University, PRC.

His research interests include computer visions, image processing and analysis, wavelet transformation theory and its application.

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Wang, QC., Gong, YH., Yang, CH. et al. Robust object tracking under appearance change conditions. Int. J. Autom. Comput. 7, 31–38 (2010). https://doi.org/10.1007/s11633-010-0031-9

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  • DOI: https://doi.org/10.1007/s11633-010-0031-9

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