Adaptive Learning of Statistical Appearance Models for 3D Human Tracking

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 over current techniques is that it provides a dense, object-based cue. Multi-dimensional histograms are used to represent feature distributions and different histogram similarity measures are evaluated. An on-line region grouping algorithm, driven by prior knowledge of clothing structure, is derived that enables better histogram estimation and greatly increases computational efficiency. Finally, we demonstrate that the smooth, broad likelihood response allows efficient inference using coarse sampling and local optimisation. Results from tracking real world sequences are presented.

• Type: Conference
• Year: 2002
• Where: BMVC
• Authors: Timothy J. Roberts, Stephen J. McKenna, Ian W. Ricketts
• Keywords: Better Histogram Estimation, BMVC 2002, Computer Vision, Histogram Similarity Measures, Likelihood Formulation