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Moving Objects Segmentation Based on Histogram for Video Surveillance

DOI: 10.5539/mas.v3n11p80

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Abstract:

The detection of moving object is one of the key techniques for video surveillance. In order to extract the moving object robustly in complex background, this paper presents a novel background subtraction method for detecting foreground objects in dynamic scenes. The difference image of color distance between current image and the reference background image in YUV color space is first obtained. According to the mono-modal feature of histogram of the difference image, an adaptive clustering method based on histogram is given. With morphological filtering, the flecks of noise existed in the segmented binary image can be removed. Finally, an updating scheme for background image is introduced to follow the variation of illumination and environmental conditions. Experimental results show that the proposed approach can detect moving objects effectively from video sequences.

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