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Moving Object Localization in Video Sequences under Static and Dynamic Background Conditions

Background subtraction is one of the most reliable approach to localize the moving object under static camera arrangement. As seen, the moving object detection is a preliminary task in many vision applications such as video analysis, object tracking and activity analysis. However, the quasi-stationary pixels, aperture effect, ghost trail and varying illumination are still an annoying factors in the extraction procedures of the actual moving object in video. To alleviate the above problems, a video segmentation method is proposed that utilizes background subtraction and the 3-class fuzzy c-means clustering algorithm for extracting the relevant moving pixels. The proposed algorithm modifies learning parameters of adaptive filters to adapt the changes in the background. Afterwards, it incorporates the Markovian framework in which the initial motion field provides a prior information to regularize the segmentation process. The method achieves better visual and quantitative performance than other well-known background subtraction methods reported in this paper.