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Multiple Faces Tracking via Statistical Appearance Model

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Proceedings of 2013 Chinese Intelligent Automation Conference

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 256))

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Abstract

Recently, appearance based methods have become a dominating trend in tracking. For example, tracking-by-detection models a target with an appearance classifier that separates it from the surrounding background. Recent advances in multi-target tracking suggest learning an adaptive appearance affinity measurement for target association. In this paper, statistical appearance model (SAM), which characterizes facial appearance by its statistics, is developed as a novel multiple faces tracking method. A major advantage of SAM is that the statistics is a target-specific and scene-independent representation, which helps for further video annotation and behavior analysis. By sharing the statistical appearance models between different videos, we are able to improve tracking stability on quality-degraded videos.

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Acknowledgments

This work is supported by the National Natural Science Foundation of China (Grant No. 60974129) and Intramural Research foundation of NJUST (2011YBXM119).

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Authors and Affiliations

  1. School of Automation, Nanjing University of Science and Technology, Nanjing, Jiangsu, China

    Jie Hou, Yaobin Mao & Jinsheng Sun

Authors
  1. Jie Hou
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  2. Yaobin Mao
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  3. Jinsheng Sun
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Corresponding author

Correspondence to Yaobin Mao .

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Editors and Affiliations

  1. , Department of Computer Science, Tsinghua University, Qinghua, Beijing, 100084, China, People's Republic

    Zengqi Sun

  2. Tsinghua University, Qinghua, Beijing, 100084, China, People's Republic

    Zhidong Deng

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Hou, J., Mao, Y., Sun, J. (2013). Multiple Faces Tracking via Statistical Appearance Model. In: Sun, Z., Deng, Z. (eds) Proceedings of 2013 Chinese Intelligent Automation Conference. Lecture Notes in Electrical Engineering, vol 256. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38466-0_51

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  • DOI: https://doi.org/10.1007/978-3-642-38466-0_51

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-38465-3

  • Online ISBN: 978-3-642-38466-0

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