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An adaptive color-based particle filter

K. Nummiaro, E. Koller-Meier and L. Van Gool
Image and Vision Computing
Vol. 21, No. 1, pp. 99-110, 2002


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.

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  author = {K. Nummiaro and E. Koller-Meier and L. Van Gool},
  title = {An adaptive color-based particle filter},
  journal = {Image and Vision Computing},
  year = {2002},
  month = {},
  pages = {99-110},
  volume = {21},
  number = {1},
  keywords = {Particle filtering, Condensation algorithm, color distribution, Bhattacharyya coefficient and mean shift tracker}