{"id":11168,"date":"2013-12-31T01:16:17","date_gmt":"2013-12-30T23:16:17","guid":{"rendered":"http:\/\/hgpu.org\/?p=11168"},"modified":"2013-12-31T01:16:17","modified_gmt":"2013-12-30T23:16:17","slug":"real-time-background-subtraction-on-gpu-using-cuda","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=11168","title":{"rendered":"Real Time Background Subtraction On GPU Using CUDA"},"content":{"rendered":"<p>Although trivial Background Subtraction algorithms which are median- based, Gaussian-based and Kernel density-based approaches can perform quite fast, but they are not roust enough to be used in various computer vision problems. Some complex algorithms usually give better results, but are too slow to be applied to real-time systems. Here, we examine the GPU architecture and describe how a background subtraction algorithm can be implemented on graphics hardware to achieve real-time performance. Experiment is performed using a low-end GeForce 8400M GS GPU that provides at least 3X speedup.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Although trivial Background Subtraction algorithms which are median- based, Gaussian-based and Kernel density-based approaches can perform quite fast, but they are not roust enough to be used in various computer vision problems. Some complex algorithms usually give better results, but are too slow to be applied to real-time systems. Here, we examine the GPU architecture [&hellip;]<\/p>\n","protected":false},"author":351,"featured_media":0,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_jetpack_memberships_contains_paid_content":false,"footnotes":"","jetpack_publicize_message":"","jetpack_publicize_feature_enabled":true,"jetpack_social_post_already_shared":true,"jetpack_social_options":{"image_generator_settings":{"template":"highway","default_image_id":0,"font":"","enabled":false},"version":2}},"categories":[36,11,73,89,3],"tags":[1787,1782,1791,14,20,977],"class_list":["post-11168","post","type-post","status-publish","format-standard","hentry","category-algorithms","category-computer-science","category-computer-vision","category-nvidia-cuda","category-paper","tag-algorithms","tag-computer-science","tag-computer-vision","tag-cuda","tag-nvidia","tag-nvidia-geforce-8400-m-gs"],"views":2742,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/11168","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/users\/351"}],"replies":[{"embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=11168"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/11168\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=11168"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=11168"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=11168"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}