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A Semantic Based Similarity Measure for Human Motion Data

In this paper, we measure the similarity of human motion data in the terms of distances between trajectories with a semantic method. In order to solve the problem of heavy computation cost, the semantic method that represents a trajectory as a set of 5-D vectors which contains the semantic information are proposed. Through experiments, the semantic method is proved to be efficient for cutting down the computation time and for two kinds of problems: overlapped trajectories with different directions and the trajectories with decoytrenches.