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Object Tracking with an Adaptive Color-Based Particle Filter

K. Nummiaro, E. Koller-Meier and L. Van Gool
Symposium for Pattern Recognition of the DAGM
Zuerich, September 2002

Abstract

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.


Download in postscript format
@InProceedings{eth_biwi_00249,
  author = {K. Nummiaro and E. Koller-Meier and L. Van Gool},
  title = {Object Tracking with an Adaptive Color-Based Particle Filter},
  booktitle = {Symposium for Pattern Recognition of the DAGM},
  year = {2002},
  month = {September},
  pages = {353-360},
  editor = {L. Van Gool},
  publisher = {Springer},
  keywords = {particle filtering, Condensation algorithm, model update, color filtering, Bhattacharyya coefficient}
}