GPU-Based Foreground-Background Segmentation Using an Extended Colinearity Criterion

Andreas Griesser, Stefaan De Roeck, Alexander Neubeck, Luc Van Gool
Swiss Federal Institute of Technology (ETH), Computer Vision Lab, Zurich, Switzerland
Proceedings of Vision, Modeling, and Visualization (VMV) 2005


   author={Andreas Griesser and Stefaan De Roeck and Alexander Neubeck and Luc Van Gool},

   title={GPU-Based Foreground-Background Segmentation using an Extended Colinearity Criterion},

   booktitle={Proceedings of Vision, Modeling, and Visualization (VMV) 2005},




   editor={G. Greiner and J. Hornegger and H. Niemann and M. Stamminger},

   publisher={IOS Press},

   keywords={foreground-background segmentation, real-time, graphics hardware, gpu}


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We present 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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