Selective Eigenbackground for Background Modeling and Subtraction in Crowded Scenes

Background subtraction is a fundamental preprocessing step in many surveillance video analysis tasks. In spite of significant efforts, however, background subtraction in crowded scenes remains challenging, especially, when a large number of foreground objects move slowly or just keep still. To address the problem, this paper proposes a selective eigenbackground method for background modeling and subtraction in crowded scenes. The contributions of our method are three-fold: First, instead of training eigenbackgrounds using the original video frames that may contain more or less foregrounds, a virtual frame construction algorithm is utilized to assemble clean background pixels from different original frames so as to construct some virtual frames as the training and update samples. This can significantly improve the purity of the trained eigenbackgrounds. Second, for a crowded scene with diversified environmental conditions (e.g., illuminations), it is difficult to use only one eigenbackground model to deal with all these variations, even using some online update strategies. Thus given several models trained offline, we utilize peak signal-to-noise ratio to adaptively choose the optimal one to initialize the online eigenbackground model. Third, to tackle the problem that not all pixels can obtain the optimal results when the reconstruction is performed at once for the whole frame, our method selects the best eigenbackground for each pixel to obtain an improved quality of the reconstructed background image. Extensive experiments on the TRECVID-SED dataset and the Road video dataset show that our method outperforms several state-of-the-art methods remarkably.