A likelihood formulation for human tracking is presented based upon matching feature statistics on the surface of an articulated 3D body model. A benefit of such a formulation ove...
Timothy J. Roberts, Stephen J. McKenna, Ian W. Ric...
This paper addresses the 3D tracking of pose and animation of the human face in monocular image sequences using deformable 3D models. For each frame, the proposed adaptation is sp...
We propose a generative statistical approach to human motion modeling and tracking that utilizes probabilistic latent semantic (PLSA) models to describe the mapping of image featu...
The ability to detect and track human heads and faces in video sequences is useful in a great number of applications, such as human-computer interaction and gesture recognition. Re...
This paper introduces a framework to track 3D human movement using Gaussian process dynamic model (GPDM) and particle filter. The framework combines the particle filter and discri...