Motion recognition from video sequences

Authors:
Xiang Yu;Simon X. Yang
Affiliations:
Advanced Robotics and Intelligent Systems, Lab School of Engineering, University of Guelph, Guelph, ON, Canada;Advanced Robotics and Intelligent Systems, Lab School of Engineering, University of Guelph, Guelph, ON, Canada
Venue:
AI'03 Proceedings of the 16th Canadian society for computational studies of intelligence conference on Advances in artificial intelligence
Year:
2003

Citing 4
Cited 0

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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.