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| Motion Activity Recognition Based on Abstract Hidden Markov Model |
| QIAN Kun, MA Xu-Dong, DAI Xian-Zhong |
| Key Laboratory of Measurement and Control of Complex Systems of Engineering, Ministry of Education, School of Automation, Southeast University, Nanjing 210096 |
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Abstract Recognition of human motion activity is essential in home-care robotic systems. In this paper, a probabilistic approach is proposed for human motion activity recognition based on the abstract hidden Markov model (AHMM). The AHMM is a well-suited hierarchical model for representing goal-directed motions at different levels of abstraction. In this model, the decision making process of agent is equivalent to an abstract Markov decision process (MDP). A model learning method is presented based on expectation-maximization algorithm to learn the observation model and the transition model respectively. Moreover, approximate inference of the AHMM is achieved by using Rao-blackwellised particle filters and thereby it enables efficient computation in recognizing motion patterns. Using trajectories derived from a visual tracking system, several indoor motion patterns are recognized. Experimental results validate the good performance of the proposed approach.
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Received: 24 March 2008
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2009, Vol. 22 
