3D perceptual shape feature-based body parts classification and pose estimation

Human body motion and gesture analysis has been boosted by the latest developments of 3D cameras and the high demands of emerging applications. Body parts classification and pose estimation are essential for the human body tracking and motion recognition. In this poster, we present a 3D perceptual shape feature-based approach for efficient body parts classification and pose estimation. The contribution of this work is twofold: 1) by utilizing 3D image features and kinematic constraints, the classification task can be efficiently performed without huge training data and costly learning process; 2) by applying the classification results, complexity of body pose estimation can be significantly reduced. Experimental results demonstrate the system performance, and exhibit the potential for complex body pose estimation and tracking.