The object detection in video streams plays an important role in computer vision applications. The background subtraction, comparing each new frame to a model of the background, is one of the most popular method. However, the static background is practically impossible, and dynamic background makes the perfect object detection difficult. In addition, the problem of static object significantly affects the performance. In the paper, based on the visual background extraction (ViBe) algorithm, we presented a new method to deal with all problems. For the dynamic scene, we presented a new update mechanism to obtain the more robust model in dynamic regions. To address the static object issue, we cancelled the propagation mechanism in ViBe, designed an algorithm to detect region where it is always detected as foreground, and distinguished the static object from ghost with a self-designed measurement, which combined the knowledge of region contrast and edge. We described our method in full details, and compared it with other background subtraction techniques. Our experimental results show that the proposed method outperforms several state-of-the-art object detection approaches. In addition, it can process 60 frames per second on an Intel i5 3.1 GHz CPU with C++ code.
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