Recognition of Human Daily Actions Based on Continuous Hidden Markov Models and Hierarchical Structure of Actions as Tree Representation

Abstract

This paper presents a recognition method of human daily-life action. The method utilizes hierarchical structure of actions and describes it as tree. We modelize actions by Continuous Hidden Markov Models which output time-series feature vectors extracted based on knowledge of human. In this method, recognition starts from the root, competes the likelihoods of child-nodes, chooses the maximum one as recognition result of the level, and goes to deeper level. The advantages of hierarchical recognition are: (1) recognition of various levels of abstraction, (2) simplification of low-level models, (3) response to novel data by decreasing degree of details. Experimental result shows that the method is able to recognize some basic human actions.