Robust detection of moving objects in video sequences through rough set theory framework
Image and Vision Computing
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In this paper, an efficient approach for background modeling and subtraction is proposed. It's based on a novel spatial-color feature extraction operator named spatial-color binary patterns(SCBP). As the name implies, features extracted by this operator include spatial texture and color information. In addition, a refine module is designed to refine the contour of moving objects. Using the proposed method, we improve the accuracy of subtracting the background and detecting moving objects in dynamic scenes. A data-driven model is used in our method. For each pixel, first, a histogram of SCBP is extracted from the circular egion, and then a model consist of several histograms is built. For a new observed frame, each pixel is labeled either background or foreground according to the matching degree between its SCBP histogram and its model, then the label is refined and finally the model of this pixel is updated. The proposed pproach is tested on challenging video sequences, which shows that the proposed method performs much better than several texture-based methods.