Object Tracking with an Adaptive Color-Based Particle Filter

Color can provide an efficient visual feature for tracking non-rigid objects in real-time. However, the color of an object can vary over time dependent on the illumination, the visual angle and the camera parameters. To handle these appearance changes a color-based target model must be adapted during temporally stable image observations. This paper presents the integration of color distributions into particle filtering and shows how these distributions can be adapted over time. A particle filter tracks several hypotheses simultaneously and weights them according to their similarity to the target model. As similarity measure between two color distributions the popular Bhattacharyya coefficient is applied. In order to update the target model to slowly varying image conditions, frames where the object is occluded or too noisy must be discarded.