Capturing Human Motion based on Modified Hidden Markov Model in Multi-View Image Sequences

Abstract

Human motion capturing is of great importance in video information retrieval, hence, in this paper, we propose a novel approach to effectively capturing human motions based on modified hidden markov model from multi-view image sequences. Firstly, the structure of the human skeleton model is illustrated, which is extended from skeleton root and spine root, and this skeleton consists of right leg, left leg and spine. Secondly, our proposed human motion capturing system is made up of data training module and human motion capturing module. In the data training module, multi-views motion information is extracted from a human motion database, and feature database of human motion capturing is constructed through combining multi-views motions. In the human motion capturing module, results of motion capturing can be achieved through motion classification based on a modified hidden markov model. Thirdly, the modified hidden markov model is designed by utilizing the fuzzy measure, fuzzy integer, and fuzzy intersection operator through a scaling process. Finally, a standard motion capture dataset- MPI08_Database is utilized to make performance evaluation. Compared with the existing methods, the proposed approach can effectively capture human motions with high precision