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Abstract

A system and method implemented as a software tool for foreground segmentation of video sequences in real-time, which uses two Competing 1-class Support Vector Machines (C-1SVMs) operating to separately identify background and foreground. A globalized, weighted optimizer may resolve unknown or boundary conditions following convergence of the C-1SVMs. The objective of foreground segmentation is to extract the desired foreground object from live input videos, with fuzzy boundaries captured by freely moving cameras. The present disclosure proposes the method of training and maintaining two competing classifiers, based on Competing 1-class Support Vector Machines (C-1SVMs), at each pixel location, which model local color distributions for foreground and background, respectively. By introducing novel acceleration techniques and exploiting the parallel structure of the algorithm (including reweighing and max-pooling of data), real-time processing speed is achieved for VGA-sized videos.

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