Using Hierarchical Models for 3D Human Body-Part Tracking

Human body pose estimation and tracking is a challenging task mainly because of the high dimensionality of the human body model. In this paper we introduce a Hierarchical Annealing Particle Filter (H-APF) algorithm for 3D articulated human body-part tracking. The method exploits Hierarchical Human Body Model (HHBM) in order to perform accurate body pose estimation. The method applies nonlinear dimensionality reduction combined with the dynamic motion model and the hierarchical body model. The dynamic motion model allows to make a better pose prediction, while the hierarchical model of the human body expresses conditional dependencies between the body parts and also allows us to capture properties of separate parts. The improved annealing approach is used for the propagation between different body models and sequential frames. The algorithm was checked on HumanEvaI and HumanEvaII datasets, as well as on other videos and proved to be effective and robust and was shown to be capable of performing an accurate and robust tracking. The comparison to other methods and the error calculations are provided.