An adaptive mixture color model for robust visual tracking

Global color characterization is a very powerful tool to model in a simple yet discriminant way the visual appearance of complex objects. A fixed reference model of this type can be used within both deterministic and probabilistic sequential estimation frameworks to track targets that undergo drastic changes of detailed appearance. However, changes of illumination as well as occlusions require that reference model is updated while avoiding drift. Within the particle filtering framework, we propose to address this adaptation problem using a dynamic mixture of color models with two components which are respectively fixed and rapidly updated. The merit of this approach is demonstrated on tracking players in team sport videos.