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Skeleton-based Walking Motion Analysis Using Hidden Markov Model and Active Shape Models

This paper proposes a skeleton-based human walking motion analysis system which consists of three major phases. In the first phase, it extracts the human body skeleton from the background and then obtains the body signatures. In the second phase, it analyzes the training sequences to generate statistical models. In the third phase, it uses the trained models to recognize the input human motion sequence and calculate the motion parameters. The experimental results demonstrate how our system can recognize the motion type and describe the motion characteristics of the image sequence. Finally, the synthesized motion sequences are illustrated. The major contributions of this paper are: (1) development of a skeleton-based method and use of Hidden Markov Models (HMM) to recognize the motion type; (2) incorporation of the Active Shape Models (ASMs) and the body structure characteristics to generate the motion parameter curves of the human motion.