3D Action Modeling and Reconstruction for 2D Human Body Tracking

In this paper we present a technique for predicting the 2D human body joints and limbs position in monocular image sequences, and reconstructing its corresponding 3D postures using information provided by a 3D action model. This method is used in a framework based on particle filtering, for the automatic tracking and reconstruction of the 3D human body postures. A set of the reconstructed postures up to time t are projected on the action space defined in this work, which is learnt from Motion Capture data, and provides us a principled way to establish similarity between body postures, natural occlusion handling, invariance to viewpoint, robustness, and is able to handle different people and different speeds while performing an action. Results on manually selected joint positions on real image sequences are shown in order to prove the correctness of this approach.