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Pseudo Measurement Based Multiple Model Approach for Robust Player Tracking

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Computer Vision – ACCV 2006 (ACCV 2006)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 3852))

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Abstract

This paper presents a robust player tracking method for sports video analysis. In order to track agile player stably and robustly, we employ multiple models method, with a mean shift procedure corresponding to each model for player localization. Furthermore, we define pseudo measurement via fusing the measurements obtained by mean shift procedure. And the fusing coefficients are built from two likelihood functions: one is image based likelihood; the other is motion based association probability. Experimental results show effectiveness of our method in the hard case of player tracking literature.

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Author information

Authors and Affiliations

  1. Institute of Artificial Intelligence and Robotics, Xi’an JiaoTong University, Xi’an, China

    Xiaopin Zhong, Nanning Zheng & Jianru Xue

Authors
  1. Xiaopin Zhong
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  2. Nanning Zheng
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  3. Jianru Xue
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Editor information

Editors and Affiliations

  1. Center for Visual Information Technology, International Institute of Information Technology, Hyderabad, India

    P. J. Narayanan

  2. Department of Computer Science, Columbia University, 500 West 120th Street, NY 10027, New York, USA

    Shree K. Nayar

  3. Microsoft Research Asia, P.O. Box, Beijing, P.R. China

    Heung-Yeung Shum

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© 2006 Springer-Verlag Berlin Heidelberg

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Zhong, X., Zheng, N., Xue, J. (2006). Pseudo Measurement Based Multiple Model Approach for Robust Player Tracking. In: Narayanan, P.J., Nayar, S.K., Shum, HY. (eds) Computer Vision – ACCV 2006. ACCV 2006. Lecture Notes in Computer Science, vol 3852. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11612704_78

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  • DOI: https://doi.org/10.1007/11612704_78

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-31244-4

  • Online ISBN: 978-3-540-32432-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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