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GPU-based non-parametric background subtraction for a practical surveillance system

D. Schreiber, M. Rauter
Safety & Security Dept., AIT Austrian Inst. of Technol. GmbH, Vienna, Austria
IEEE 12th International Conference on Computer Vision Workshops (ICCV Workshops), 2009

@conference{schreiber2009gpu,

   title={GPU-based non-parametric background subtraction for a practical surveillance system},

   author={Schreiber, D. and Rauter, M.},

   booktitle={Computer Vision Workshops (ICCV Workshops), 2009 IEEE 12th International Conference on},

   pages={870–877},

   year={2009},

   organization={IEEE}

}

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In this paper we present a background subtraction algorithm for a practical surveillance system, on a GPU. It utilizes a compressed non-parametric representation of the history of each pixel, using YCbCr color space, not requiring an offline training period. Although it can be parametrized to cope successfully with moving background, we rather focus on fulfilling some requirements of a practical surveillance system monitoring pedestrians and traffic. First, the time it takes for a stopped foreground object to be absorbed into the background (integration time) should be large enough. Furthermore, the integration time should be controllable by the user and should remain constant, regardless of the complexity of the scene. A further requirement is that objects which repeatedly re-appear in the image, e.g. vehicles having similar colors crossing repeatedly the same region in the image, need not be incorporated into the background. In addition, foreground aperture is undesired, even in case of slowly moving large objects. We implement our method on a NVidia GeForce 9800 GT GPU, achieving 635 fps for the background algorithm, or 436 fps when memory transfer to and from the GPU is included, on a video with 352×288 resolution. We demonstrate the capability of the algorithm by comparing it to MoG, both on moving background and on practical surveillance scenarios. Our method outperforms MoG in both modes, in terms of adaptation speed, run-time and the quality of the foreground segmentation. Furthermore, the integration time is more stable.
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