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
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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2009, Vol. 22 
