Selective eigenbackgrounds method for background subtraction in crowed scenes
In this paper, a selective eigenbackgrounds method is proposed for background subtraction in crowded scenes. In order to train and update the eigenbackground model with frames containing few objects (i.e. clean frames), virtual frames are constructed based on a frame selection map. Then, the eigenbackground that best depicts background is selected for each pixel based on an eigenbackground selection map. Experimental results show the performance of the proposed method is better than those of some state-of-the-art methods in crowded scenes.