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Bayesian Pixel Classification for Human Tracking

D. Roth, P. Doubek and L. Van Gool
IEEE Workshop on Motion and Video Computing (MOTION)
Breckenridge, Colorado, January 2005

Abstract

We present a monocular object tracker, able to detect and track multiple objects in non-controlled environments. Bayesian per-pixel classification is used to build a tracking framework that segments an image into foreground and background objects, based on observations of object appearances and motions. Gaussian mixtures are used to build the color appearance models. The system adapts to changing lighting conditions, handles occlusions, and works in real-time.


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@InProceedings{eth_biwi_00321,
  author = {D. Roth and P. Doubek and L. Van Gool},
  title = {Bayesian Pixel Classification for Human Tracking},
  booktitle = {IEEE Workshop on Motion and Video Computing (MOTION)},
  year = {2005},
  month = {January},
  pages = {78-83},
  publisher = {IEEE},
  keywords = {tracking, segmentation}
}