Human Body Parts Tracking Using Sequential Markov Random Fields

Abstract

Automatically tracking human body parts is a difficult problem because of background clutters, missing body parts, and the high degrees of freedoms and complex kinematics of the articulated human body. This paper presents the sequential Markov random fields (SMRFs) for tracking and labeling moving human body parts automatically by learning the spatio-temporal structures of human motions in the setting of occlusions and clutters. We employ a hybrid strategy, where the temporal dependencies between two successive human poses are described by the sequential Monte Carlo method, and the spatial relationships between body parts in a pose is described by the Markov random fields. Efficient inference and learning algorithms are developed based on the relaxation labeling. Experimental results show that the SMRF can effectively track human body parts in natural scenes.