Adaptive background mixture models for real-time tracking Export

@inproceedings{citeulike:605862, abstract = {A common method for real-time segmentation of moving regions in image sequences involves ” background subtraction,” or thresholding the error between an estimate of the image without moving objects and the current image. The numerous approaches to this problem differ in the type of background model used and the procedure used to update the model. This paper discusses modeling each pixel as a mixture of Gaussians and using an on-line approximation to update the model. The Gaussian distributions of the adaptive mixture model are then evaluated to determine which are most likelyt o result from a background process. Each pixel is classified based on whether the Gaussian distribution which represents it most effectively is considered part of the background model. This results in a stable, real-time outdoor tracker which reliablyde als with lighting changes, repetitive motions from clutter, and long-term scene changes. This system has been run almost continuously for 16 months, 24 hours a day, through rain and snow.}, author = {Stauffer, C. and Grimson, W.}, booktitle = {CVPR}, citeulike-article-id = {605862}, citeulike-linkout-0 = {http://www.ai.mit.edu/projects/vsam/Publications/stauffer_cvpr98_track.pdf}, keywords = {background-modeling, gaussian-mixture-models}, pages = {246--252}, posted-at = {2006-04-28 14:47:06}, priority = {2}, title = {Adaptive background mixture models for real-time tracking}, url = {http://www.ai.mit.edu/projects/vsam/Publications/stauffer_cvpr98_track.pdf}, year = {1999} }

TY - CONF ID - citeulike:605862 L3 - citeulike-article-id:605862 N2 - A common method for real-time segmentation of moving regions in image sequences involves “background subtraction,” or thresholding the error between an estimate of the image without moving objects and the current image. The numerous approaches to this problem differ in the type of background model used and the procedure used to update the model. This paper discusses modeling each pixel as a mixture of Gaussians and using an on-line approximation to update the model. The Gaussian distributions of the adaptive mixture model are then evaluated to determine which are most likelyt o result from a background process. Each pixel is classified based on whether the Gaussian distribution which represents it most effectively is considered part of the background model. This results in a stable, real-time outdoor tracker which reliablyde als with lighting changes, repetitive motions from clutter, and long-term scene changes. This system has been run almost continuously for 16 months, 24 hours a day, through rain and snow. EP - 252 TI - Adaptive background mixture models for real-time tracking T2 - CVPR SP - 246 KW - background-modeling KW - gaussian-mixture-models AU - Stauffer, C AU - Grimson, W PY - 1999/// UR - http://www.ai.mit.edu/projects/vsam/Publications/stauffer_cvpr98_track.pdf ER -