Emulating human perception of motion similarity
Evaluating the similarity of motions is useful for motion retrieval, motion blending, and performance analysis of dancers and athletes. Euclidean distance between corresponding joints has been Widely adopted in measuring similarity of postures and hence motions. However, such a measure does not necessarily conform to the human perception of motion similarity. In this paper, eve propose a new similarity measure based on machine learning techniques. We make: rise of the results of questionnaires from subjects answering whether arbitrary pairs of motions appear similar or not. Using tire relative distance between the joints as the basic features, we train the system to compute tire similarity of arbitrary pair of motions. Experimental results show that our method outperforms methods based on Euclidean distance between corresponding joints. Our method is applicable to content-based motion retrieval of human motion for large-scale database systems. It is also applicable to e-Learning systems which automatically evaluates the performance of dancers and athletes by comparing tire subjects' motions With those by experts. Copyright (C) 2008 John Wiley & Sons, Ltd.
human perception 3d human motion similarity pattern recognition