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Object Tracking Using Mean Shift for Adaptive Weighted-Sum Histograms

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Abstract

An object tracking method is proposed that uses mean shift for adaptive weighted-sum histograms and makes an attempt to incorporate contourlet histograms and spatial histograms. The tracking results of the contourlet-based method and the spatial-histogram-based method have been incorporated with adaptive weights. We employ the Bhattacharyya distance to evaluate the values of the weights. The experimental results indicate that the proposed method is robust for small targets, occlusion, rotation, scale transformation, or complex backgrounds, and is superior to three methods based on mean shift and a tracking method based on particle filters.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (Grant No. 61001179, No. 61201393), by the Natural Science Foundation of Guangdong Province, China (No. S2011040004079), and by the Project on integration of production, education and research, Guangdong Province and Ministry of Education, China (2012B091100424).

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Authors and Affiliations

  1. School of Information Engineering, Guangdong University of Technology, 510006, Guangzhou, China

    Nian Cai, Nannan Zhu, Wenting Guo, Bingo Wing-Kuen Ling & Qingyun Dai

  2. Guangdong Provincial Key Laboratory of Micro-nano Manufacturing Technology and Equipment, Guangdong University of Technology, 510006, Guangzhou, China

    Han Wang

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  1. Nian Cai
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  2. Nannan Zhu
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  3. Wenting Guo
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  4. Bingo Wing-Kuen Ling
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  5. Han Wang
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  6. Qingyun Dai
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Correspondence to Bingo Wing-Kuen Ling.

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Cai, N., Zhu, N., Guo, W. et al. Object Tracking Using Mean Shift for Adaptive Weighted-Sum Histograms. Circuits Syst Signal Process 33, 483–499 (2014). https://doi.org/10.1007/s00034-013-9649-5

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