3-D Body Posture Tracking For Human Action Template Matching

In this paper we present a novel approach to 3-D human action classification based on the analysis of volumetric data obtained form the joint processing of video sequences acquired by a multiple-camera system. The use of volumetric data makes the system very robust and avoids problems related the typical human body self-occlusions and motion ambiguities, very common in an independent camera-by-camera analysis. A shape descriptor of a human body is obtained in order to capture only posture-dependent characteristics and its outputs at each time instant are collected together in action feature matrices. The use of dynamic time warping approach for action template matching accounts for possible temporal nonlinear distortions among different instances of the same gesture and allows gesture classification