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Abstract
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Segmenting moving objects from various video streams especially in the presence of complex dynamic backgrounds is one of the most pivotal and fundamental tasks in computer vision. Although there are many high quality proposals for moving object detection and segmentation, still some issues such as sudden changes in illumination, camera jitter, swaying vegetation, rippling water, etc need to be resolved. This paper proposes a new method for moving object detection using background subtraction technique. To this end, a new color feature descriptor and a new model updating strategy are proposed for ameliorating the effects of these natural challenges. The proposed descriptor is a local fuzzy descriptor that tries to extract the color feature of a pixel by looking at its neighbors. Because of its local nature, this descriptor is able to accurately detect and extract moving objects. The fuzzy nature of the descriptor helps it to be robust against diverse challenging situations. In order to validate the functionality and show the supremacy of the proposed method, comprehensive experiments were conducted on various challenging datasets. From the experimental results, it is observed that the proposed method achieves acceptable results for moving object detection and segmentation.
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