Conditional Visual Tracking in Kernel Space

We consider the problem of inferring 3D articulated human motion from monocular video. This research topic has applications for scene understanding including human-computer in- terfaces, markerless human motion capture, entertainment and surveillance. A monocular approach is relevant because in real-world settings the human body parts are rarely com- pletely observed even when using multiple cameras. This is due to occlusions form other people or objects in the scene. A robust system has to necessarily deal with incomplete, ambiguous and uncertain measurements. Methods for 3D human motion reconstruction can be classified as generative and discriminative. They both require a state representation, namely a 3D human model with kinematics (joint angles) or shape (surfaces or joint po- sitions) and they both use a set of image features as observations for state inference.