Xiang Yu
ABSTRACT
This paper proposes a method for recognizing human motions from video sequences, based on the 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. A decimated magnitude spectrum is calculated from the Fourier transform of the edge images. Then, 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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