Wallflower: Principles and Practice of Background Maintenance Export

@article{citeulike:605879, abstract = {Background maintenance is a frequent element of video surveillance systems. We develop Wallflower, a threecomponent system for background maintenance: the pixellevel 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.}, author = {Toyama, Kentaro and Krumm, John and Brumitt, Barry and Meyers, Brian}, booktitle = {Seventh International Conference on Computer Vision}, citeulike-article-id = {605879}, citeulike-linkout-0 = {http://csdl2.computer.org/persagen/DLAbsToc.jsp?resourcePath=/dl/proceedings/&toc=comp/proceedings/iccv/1999/0164/01/0164toc.xml&DOI=10.1109/ICCV.1999.791228}, keywords = {background-modeling, gaussian-mixture-models}, pages = {255+}, posted-at = {2006-04-28 15:07:09}, priority = {2}, title = {Wallflower: Principles and Practice of Background Maintenance}, url = {http://csdl2.computer.org/persagen/DLAbsToc.jsp?resourcePath=/dl/proceedings/&toc=comp/proceedings/iccv/1999/0164/01/0164toc.xml&DOI=10.1109/ICCV.1999.791228}, volume = {1}, year = {1999} }

TY - JOUR ID - citeulike:605879 L3 - citeulike-article-id:605879 N2 - Background maintenance is a frequent element of video surveillance systems. We develop Wallflower, a threecomponent system for background maintenance: the pixellevel 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. TI - Wallflower: Principles and Practice of Background Maintenance VL - 1 T2 - Seventh International Conference on Computer Vision SP - 255 KW - background-modeling KW - gaussian-mixture-models AU - Toyama, Kentaro AU - Krumm, John AU - Brumitt, Barry AU - Meyers, Brian PY - 1999/// UR - http://csdl2.computer.org/persagen/DLAbsToc.jsp?resourcePath=/dl/proceedings/&toc=comp/proceedings/iccv/1999/0164/01/0164toc.xml&DOI=10.1109/ICCV.1999.791228 ER -