{"id":2550,"date":"2011-01-20T11:01:33","date_gmt":"2011-01-20T11:01:33","guid":{"rendered":"http:\/\/hgpu.org\/?p=2550"},"modified":"2011-01-20T11:01:33","modified_gmt":"2011-01-20T11:01:33","slug":"simultaneous-and-fast-3d-tracking-of-multiple-faces-in-video-by-gpu-based-stream-processing","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=2550","title":{"rendered":"Simultaneous and fast 3D tracking of multiple faces in video by GPU-based 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. Using stream processors for performing the computations as well as 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 technology, we can achieve real-time performance even when tracking multiple objects in high-quality videos.<\/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. Using stream processors for performing the computations as well as 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 [&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":[89,33,3],"tags":[14,1786,20,183,401,402],"class_list":["post-2550","post","type-post","status-publish","format-standard","hentry","category-nvidia-cuda","category-image-processing","category-paper","tag-cuda","tag-image-processing","tag-nvidia","tag-nvidia-geforce-8800-gtx","tag-particle-filtering","tag-video-tracking"],"views":2080,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/2550","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=2550"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/2550\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=2550"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=2550"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=2550"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}