Robust and efficient foreground analysis for real-time video surveillance

We present a new method to robustly and efficiently analyze foreground when we defect 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 [11]. 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 - fragmentation (one object becomes multiple parts). The method was tested on our real time video surveillance system. It is robust and run about 130 fps for color images and 150 fps for grayscale images at size 160x120 on a 2GB Pentium IV machine with MMX optimization.