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Human behavior classification by analyzing periodic motions

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Abstract

Recognizing human action is a critical step in many computer vision applications. In this paper, the problem of human behavior classification is addressed from a periodic motion analysis viewpoint. Our approach uses human silhouettes as motion features that can be obtained efficiently, and then projected it into a lower dimensional space where matching is performed. After a periodic analysis, each action unit is represented as a closed loop in this lower dimensional space, and matching is done by computing the distances among these loops. The main contributions are twofold: (1) an efficient periodic action feature constructing method is introduced; and (2) the difference between action units with different phase is computed adaptively with a novel distance proposed in this work. To demonstrate the effectiveness of this approach, human behavior classification experiments were performed on an open dataset. Classification results are highly accurate and show that this approach is promising and efficient.

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References

  1. Hsieh J W, Hsu Y T, Liao H-Y M. Video-based human movement analysis and its application to surveillance systems. IEEE Transactions on Multimedia, 2008, 10(3): 372–384

    Article  Google Scholar 

  2. Lin W, Sun M T, Poovandran R. Activity recognition using a combination of category components and local models for video surveillance. IEEE Transaction on Circuits and Systems for Video Technology, 2008, 18(8): 1128–1139

    Article  Google Scholar 

  3. Choi J, Cho Y I, Han T. A view-based real-time human action recognition system as an interface for human computer interaction. In: Proceedings of 13th International Conference on Virtual Systems and Multimedia, 112–120

  4. Su C W, Liao H-Y M, Tyan H R. Motion flow-based video retrieval. IEEE Transactions on Multimedia, 2007, 9(6): 1193–1201

    Article  Google Scholar 

  5. Aggarwal J K, Cai Q. Human motion analysis: a review. Computer Vision and Image Understanding, 1999, 73(3): 295–304

    Article  Google Scholar 

  6. Moeslund T, Granum E. A survey of computer vision based human motion capture. Computer Vision and Image Understanding, 2001, 81(3): 231–268

    Article  MATH  Google Scholar 

  7. Wang L, Hu W, Tan T. Recent developments in human motion analysis. Pattern Recognition, 2003, 36(3): 585–601

    Article  Google Scholar 

  8. Polana R, Nelson R. Detecting activities. In: Proceedings of Computer Vision and Pattern Recognition, 1993, 2–7

  9. Niyogi S, Adelson E. Analyzing and recognizing walking figures in xyt. In: Proceedings of Computer Vision and Pattern Recognition, 1994, 469–474

  10. Baumberg A, Hogg D. An efficient method for contour tracking using active shape models. In: Proceedings of IEEE Workshop on Motion of Non-Rigid and Articulated Objects, 1994, 194–199

  11. Park S, Aggarwal J K. Recognition of human interaction using multiple features in grayscale images. In: Proceedings of 15th International Conference on Pattern Recognition, 2000, 1: 51–54

    MATH  Google Scholar 

  12. Dever J, da VitoriaLobo N, Shah M. Automatic visual recognition of armed robbery. In: Proceedings of IEEE International Conference on Pattern Recognition, 2002, 451–455

  13. Zelnik-Manor L, Irani M. Statistical analysis of dynamic actions. IEEE Transactions on Pattern Recognition and Machine Intelligence, 2006, 28(9): 1530–1535

    Article  Google Scholar 

  14. Wang L, Suter D. Analyzing human movements from silhouettes using manifold learning. In: Proceedings of IEEE International Conference on Video and Signal Based Surveillance, 2006, 7–7

  15. Yacoob Y, Black M J. Parameterized modeling and recognition of activities. Computer Vision Image Understanding, 1999, 73(2): 232–247

    Article  Google Scholar 

  16. Murase H, Sakai R. Moving object recognition in eigenspace representation: gait analysis and lip reading. Pattern Recognition Letter, 1996, 17(2): 155–162

    Article  Google Scholar 

  17. Masoud O, Papanikolopoulos N. A method for human action recognition. Image and Vision Computing, 2003, 21(8): 729–743

    Article  Google Scholar 

  18. Stauffer C, Grimson W. Adaptive background mixture models for real-time tracking. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 1999, 246–252

  19. Cutler R, Davis L. Robust real-time periodic motion detection, analysis, and applications. IEEE Transactions on Pattern Recognition and Machine Intelligence, 2000, 22(8): 781–796

    Article  Google Scholar 

  20. Rao C, Yilmaz A, Shah M. View-invariant representation and recognition of actions. International Journal of Computer Vision, 2002, 50(2): 203–226

    Article  MATH  Google Scholar 

  21. Turk M, Pentland A. Eigenfaces for recognition. Journal of Cognitive Neuroscience, 1991, 13(1): 71–86

    Article  Google Scholar 

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Authors and Affiliations

  1. School of Physical and Electronic Information, Huaibei Coal Industry Teachers College, Huaibei, 235000, China

    Jiangtao Wang & Debao Chen

  2. School of Computer Science and Technology, Nanjing University of Science and Technology, Nanjing, 210094, China

    Jingyu Yang

Authors
  1. Jiangtao Wang
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  2. Debao Chen
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  3. Jingyu Yang
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Correspondence to Jiangtao Wang.

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Wang, J., Chen, D. & Yang, J. Human behavior classification by analyzing periodic motions. Front. Comput. Sci. China 4, 580–588 (2010). https://doi.org/10.1007/s11704-009-0070-y

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  • DOI: https://doi.org/10.1007/s11704-009-0070-y

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