{"id":6078,"date":"2011-10-27T11:17:27","date_gmt":"2011-10-27T08:17:27","guid":{"rendered":"http:\/\/hgpu.org\/?p=6078"},"modified":"2011-10-27T11:17:27","modified_gmt":"2011-10-27T08:17:27","slug":"efficient-implementation-of-optical-flow-algorithm-based-on-directional-filters-on-a-gpu-using-cuda","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=6078","title":{"rendered":"Efficient Implementation of Optical Flow Algorithm Based on Directional Filters on a GPU Using CUDA"},"content":{"rendered":"<p>This paper describes an optical flow estimation algorithm using directional filters and an AM-FM demodulation algorithm, and its efficient implementation on a NVIDIA GPU using CUDA. The resulting implementation is several thousand times faster than the corresponding MATLAB code, which makes the described scheme suitable for real-time applications. This paper also describes a new multiresolution scheme for our algorithm which allows estimating high speeds without aliasing. The accuracy of our algorithm has proved to be comparable to the accuracy of well-known optical flow algorithms.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>This paper describes an optical flow estimation algorithm using directional filters and an AM-FM demodulation algorithm, and its efficient implementation on a NVIDIA GPU using CUDA. The resulting implementation is several thousand times faster than the corresponding MATLAB code, which makes the described scheme suitable for real-time applications. This paper also describes a new multiresolution [&hellip;]<\/p>\n","protected":false},"author":351,"featured_media":0,"comment_status":"open","ping_status":"open","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":false,"jetpack_social_options":{"image_generator_settings":{"template":"highway","default_image_id":0,"font":"","enabled":false},"version":2}},"categories":[36,11,73,89,3],"tags":[1787,1782,1791,14,20,253,896,176],"class_list":["post-6078","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-gtx-260","tag-optical-flow","tag-package"],"views":2305,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/6078","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=6078"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/6078\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=6078"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=6078"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=6078"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}