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A study of motion recognition from video sequences

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Computing and Visualization in Science

Abstract

This paper proposes a method for recognizing human motions from video sequences, based on the cognitive hypothesis that there exists a repertoire of movement primitives in biological sensory motor systems. First, a content-based image retrieval algorithm is used to obtain statistical feature vectors from individual images. An unsupervised learning algorithm, self-organizing map, is employed to cluster these shape-based features. Motion primitives are recovered by searching the resulted time serials based on the minimum description length principle. Experimental results of motion recognition from a 37 seconds video sequence show that the proposed approach can efficiently recognize the motions, in a manner similar to human perception.

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Authors and Affiliations

  1. Advanced Robotics and Intelligent Systems (ARIS) Lab, School of Engineering, University of Guelph, Guelph, N1G 2W1, Ontario, Canada

    Xiang Yu & Simon X. Yang

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  1. Xiang Yu
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  2. Simon X. Yang
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Correspondence to Simon X. Yang.

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Communicated by

G. Wittum

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Yu, X., Yang, S. A study of motion recognition from video sequences. Comput. Visual Sci. 8, 19–25 (2005). https://doi.org/10.1007/s00791-004-0143-2

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  • DOI: https://doi.org/10.1007/s00791-004-0143-2

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