We present a new method to robustly and efficiently analyze foreground when we detect background for a fixed camera view by using mixture of Gaussians models and multiple cues. The background is modeled by three Gaussian mixtures as in the work of Stauffer and Grimson (1999). Then the intensity and texture information are integrated to remove shadows and to enable the algorithm working for quick lighting changes. For foreground analysis, the same Gaussian mixture model is employed to detect the static foreground regions without using any tracking or motion information. Then the whole static regions are pushed back to the background model to avoid a common problem in background subtraction /spl times/ fragmentation (one object becomes multiple parts). The method was tested on our real time video surveillance system. It is robust and run about 130 fpsfor color images and 150 fps for grayscale images at size 160/spl times/120 on a 2GB Pentium IV machine with MMX optimization.


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    Title :

    Robust and efficient foreground analysis for real-time video surveillance


    Contributors:
    Ying-Li Tian, (author) / Lu, M. (author) / Hampapur, A. (author)


    Publication date :

    2005-01-01


    Size :

    537402 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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