Color-Based Probabilistic Tracking

Color-based trackers recently proposed in [3,4,5] have been proved robust and versatile for a modest computational cost. They are especially appealing for tracking tasks where the spatial structure of the tracked objects exhibits such a dramatic variability that trackers based on a space-dependent appearance reference would break down very fast. Trackers in [3,4,5] rely on the deterministic search of a window whose color content matches a reference histogram color model. Relying on the same principle of color histogram distance, but within a probabilistic framework, we introduce a new Monte Carlo tracking technique. The use of a particle filter allows us to better handle color clutter in the background, as well as complete occlusion of the tracked entities over a few frames. This probabilistic approach is very flexible and can be extended in a number of useful ways. In particular, we introduce the following ingredients: multi-part color modeling to capture a rough spatial layout ignore...

• Type: Conference
• Year: 2002
• Where: ECCV
• Authors: Carine Hue, Jaco Vermaak, Michel Gangnet, Patrick Pérez
• Keywords: Background Color Model, Color Histogram Distance, Computer Vision, ECCV 2002, Handle Color Clutter, Multi-part Color Modeling, Reference Histogram Color