Background Modeling and Fuzzy Clustering for Motion Detection from Video

In this paper, for the modern intelligent video surveillance, we introduce an optimizing motion detection algorithm aim at overcoming the flaw of conventional background subtraction algorithm. We combine adaptive background model in HSV color space with moving object segmentation based on fuzzy clustering to extract moving objects from frame. The adaptive background model is able to restoring the background due to the accurate description of the HSV color space, and then the moving object segmentation based on fuzzy clustering is used to distinguish the moving area and noise area by the adaptive selection of threshold. We also consider SIFT feature to improve the performance of motion detection algorithm. The experiment show that the algorithm alleviates the impairment of noise and time complexity of the motion detection algorithm is low.