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Real-Time, GPU-based Foreground-Background Segmentation

Andreas Griesser
269, 2005
Computer Vision Lab, ETH Zuerich


This report presents a GPU-based foreground-background segmentation that processes image sequences in less than 4ms per frame. Change detection wrt. the background is based on a color similarity test in a small pixel neighbourhood, and is integrated into a Bayesian estimation framework. An iterative MRF-based model is applied, exploiting parallelism on modern graphics hardware. Resulting segmentation exhibits compactness and smoothness in foreground areas as well as for inter-frame temporal contiguity. Further refinements extend the colinearity criterion with compensation for dark foreground and background areas and thus improving overall performance.

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  author = {Andreas Griesser},
  title = {Real-Time, GPU-based Foreground-Background Segmentation},
  year = {2005},
  month = {August},
  number = {269},
  institution = {Computer Vision Lab, ETH Zuerich},
  keywords = {foreground-background segmentation, real-time, graphics hardware, gpu}