A Color-based Particle Filter

Robust real-time tracking of non-rigid objects is a challenging task. Particle filtering has been proven very successful for non-linear and non-Gaussian estimation problems. However, for the tracking of non-rigid objects, the selection of reliable image features is also essential

Color Features for Tracking Non-Rigid Objects

Robust real-time tracking of non-rigid objects is a challenging task. Color distributions provide an efficient feature for this kind of tracking problems as they are robust to partial occlusion, are rotation and scale invariant and computationally efficient. This article presents the integration of color distributions into particle filtering, which has typically been used in combination with edge-based image features. Particle filters offer a probabilistic framework for dynamic state estimation and have proven to work well in cases of clutter and occlusion. To overcome the problem of appearance changes, an adaptive model update is introduced during temporally stable image observations. Furthermore, an initialization strategy is discussed since tracked objects may disappear and reappear. Keywords: particle filtering, color distribution, Bhattacharyya coefficient

An Adaptive Color-Based Particle Filter

Robust real-time tracking of non-rigid objects is a challenging task. Particle filtering has proven very successful for non-linear and non-Gaussian estimation problems. The article presents the integration of color distributions into particle filtering, which has typically been used in combination with edge-based image features. Color distributions are applied as they are robust to partial occlusion, are rotation and scale invariant and computationally efficient. As the color of an object can vary over time dependent on the illumination, the visual angle and the camera parameters, the target model is adapted during temporally stable image observations. An initialization based on an appearance condition is introduced since tracked objects may disappear and reappear. Comparisons with the mean shift tracker and a combination between the mean shift tracker and Kalman filtering show the advantages and limitations of the new approach