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.

中文翻译：

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用于视频场景局部变化检测的内核化模糊模态变化

背景减法 (BGS) 是一种流行的方案，集中体现在有关视频处理的最新文献中。在这种情况下，提出了一种基于背景减法的新型在线核化模糊模态变化方案，用于检测图像帧序列的局部变化。在所提出的方案中，使用模糊集理论对不同时间实例的时变背景进行建模。所提出的背景减法方案利用模糊模态变化作为成本函数来拟合图像帧的像素值。使用基于内核的模态变化有助于将像素值投影到更高维空间中，将它们线性地分为对象和背景类。所提出技术的结果在不同的具有挑战性的序列上得到验证，包括动态背景、相机抖动、