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A remarkable compress performance was achieved in the surveillance video coding when high-quality reconstructed background frames were referenced in long-term. However, the high-quality reconstructed background frames lead to bursting bit-rate peaks in the video transmission, which may cause obvious communication delay or buffer overflow. In order to address this problem, the paper proposes a quality-progressive...
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 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...
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