Motion Activity Recognition Based on Abstract Hidden Markov Model
Xu Ma · 2009
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.