A Fast Moving Object Detection Method via Local Neighborhood Similarity
Background subtraction is widely used in moving object detection. Pixel-based methods are sensitive to the nonstationary change of the scenes. Region-based approaches allow only coarse detection of the moving objects. In this paper, a novel algorithm based on local neighborhood similarity is proposed. Integrate the similarity of its surrounding pixels with the background model, when a pixel needs to be judged. The performance of the proposed method is evaluated by a series of indoor and outdoor experiments. Compared with the current widely used Mixture of Gaussian, the proposed algorithm in this paper achieved the perfect results in object detection and extraction.
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