{"id":2642,"date":"2011-01-27T13:25:45","date_gmt":"2011-01-27T13:25:45","guid":{"rendered":"http:\/\/hgpu.org\/?p=2642"},"modified":"2011-01-27T13:25:45","modified_gmt":"2011-01-27T13:25:45","slug":"an-hardware-architecture-for-3d-object-tracking-and-motion-estimation","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=2642","title":{"rendered":"An hardware architecture for 3D object tracking and motion estimation"},"content":{"rendered":"<p>We present a method to track and estimate the motion of a 3D object with a monocular image sequence. The problem is based on the state equations and is solved by a sequential Monte Carlo method. The method uses a CAD model of the object whose projection can be compared directly with the pixels of the image. The advantage is to obtain a better accuracy and a direct estimation of the pose and motion in the 3D world. However, this algorithm needs a massive load in computing. For real-time use, we develop in this paper a distributed algorithm that dispatches the processing between the central processing unit (CPU) and the graphics processing unit (GPU) of a consumer-market computer. Some experimental results show that it is possible to obtain an accurate 3D tracking of the object with low computing costs.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>We present a method to track and estimate the motion of a 3D object with a monocular image sequence. The problem is based on the state equations and is solved by a sequential Monte Carlo method. The method uses a CAD model of the object whose projection can be compared directly with the pixels of [&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":[36,11,3],"tags":[1787,596,1782,20,301,402],"class_list":["post-2642","post","type-post","status-publish","format-standard","hentry","category-algorithms","category-computer-science","category-paper","tag-algorithms","tag-cad","tag-computer-science","tag-nvidia","tag-nvidia-geforce-6800-gt","tag-video-tracking"],"views":2236,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/2642","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=2642"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/2642\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=2642"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=2642"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=2642"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}