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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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