{"id":151394,"date":"1999-09-01T00:00:00","date_gmt":"1999-09-01T00:00:00","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/msr-research-item\/wallflower-principles-and-practice-of-background-maintenance\/"},"modified":"2018-10-16T20:38:47","modified_gmt":"2018-10-17T03:38:47","slug":"wallflower-principles-and-practice-of-background-maintenance","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/wallflower-principles-and-practice-of-background-maintenance\/","title":{"rendered":"Wallflower: Principles and Practice of Background Maintenance"},"content":{"rendered":"<div class=\"asset-content\">\n<p>Background maintenance is a frequent element of video surveillance systems. We develop Wallflower, a three-component system for background maintenance: the pixel-level component performs Wiener filtering to make probabilistic predictions of the expected background; the region-level component fills in homogeneous regions of foreground objects; and the frame-level component detects sudden, global changes in the image and swaps in better approximations of the background. We compare our system with 8 other background subtraction algorithms. Wallflower is shown to outperform previous algorithms by handling a greater set of the difficult situations that can occur. Finally, we analyze the experimental results and propose normative principles for background maintenance.<\/p>\n<\/div>\n<p><!-- .asset-content --><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Background maintenance is a frequent element of video surveillance systems. We develop Wallflower, a three-component system for background maintenance: the pixel-level component performs Wiener filtering to make probabilistic predictions of the expected background; the region-level component fills in homogeneous regions of foreground objects; and the frame-level component detects sudden, global changes in the image and 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