{"id":1127,"date":"2010-11-03T11:46:43","date_gmt":"2010-11-03T11:46:43","guid":{"rendered":"http:\/\/hgpu.org\/?p=1127"},"modified":"2010-11-03T11:46:43","modified_gmt":"2010-11-03T11:46:43","slug":"real-time-visual-tracker-by-stream-processing","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=1127","title":{"rendered":"Real-time Visual Tracker by Stream Processing"},"content":{"rendered":"<p>In this work, we implement a real-time visual tracker that targets the position and 3D pose of objects in video sequences, specifically faces. The use of stream processors for the computations and efficient Sparse-Template-based particle filtering allows us to achieve real-time processing even when tracking multiple objects simultaneously in high-resolution video frames. Stream processing is a relatively new computing paradigm that permits the expression and execution of data-parallel algorithms with great efficiency and minimum effort. Using a GPU (graphics processing unit, a consumer-grade stream processor) and the NVIDIA CUDA<\/p>\n","protected":false},"excerpt":{"rendered":"<p>In this work, we implement a real-time visual tracker that targets the position and 3D pose of objects in video sequences, specifically faces. The use of stream processors for the computations and efficient Sparse-Template-based particle filtering allows us to achieve real-time processing even when tracking multiple objects simultaneously in high-resolution video frames. Stream processing is [&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":[11,89,3],"tags":[403,1782,14,20,183,401,402],"class_list":["post-1127","post","type-post","status-publish","format-standard","hentry","category-computer-science","category-nvidia-cuda","category-paper","tag-cmp","tag-computer-science","tag-cuda","tag-nvidia","tag-nvidia-geforce-8800-gtx","tag-particle-filtering","tag-video-tracking"],"views":2297,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/1127","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=1127"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/1127\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=1127"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=1127"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=1127"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}