Kernelized Fuzzy Modal Variation for Local Change Detection From Video Scenes

Background subtraction (BGS) is a popular scheme epitomized in the state-of-the-art literature on video processing. In this context, a novel online kernelized fuzzy modal variation based background subtraction scheme for detecting local changes from the sequences of image frames is proposed. In the proposed scheme, the time varying background at different instances of time are modeled using fuzzy set theory. The proposed background subtraction scheme, utilizes the fuzzy modal variation as the cost function for fitting the pixel values of the image frames. The use of kernel based modal variation helps in projecting the pixel values in a higher dimensional space, linearly separating them into object and background classes. The results of the proposed technique is verified on different challenging sequences including dynamic background, camera jitter, noise, blurred scene, etc. The proposed technique is successfully tested over several test sequences with two major databases (all sequences) and it provides better results compared to the twenty one existing state-of-the-art techniques.