{"id":8322,"date":"2012-10-05T19:35:29","date_gmt":"2012-10-05T16:35:29","guid":{"rendered":"http:\/\/hgpu.org\/?p=8322"},"modified":"2012-10-05T19:35:29","modified_gmt":"2012-10-05T16:35:29","slug":"is-the-game-worth-the-candle-evaluation-of-opencl-for-object-detection-algorithm-optimization","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=8322","title":{"rendered":"Is the game worth the candle? Evaluation of OpenCL for object detection algorithm optimization"},"content":{"rendered":"<p>In this paper we present out experiences with the implementation of an object detector using OpenCL. With this implementation we fullfil the need for fast and robust object detection, necessary in many applications in multiple domains (surveillance, traffic, image retrieval, &#8230;). The algorithm lends itself to be implemented in a parallel way. We exploit this opportunity by implementing it on a GPU. For this implementation, we have choosen to use the OpenCL programming language, since this allows for scalability to more performant and different types of hardware, with minimal changes to the implementation. We will discuss how the parallelization is done, and discuss the challenges we met. We will also discuss the experimental timing results we achieved and evaluate the ease-of-use of OpenCL.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>In this paper we present out experiences with the implementation of an object detector using OpenCL. With this implementation we fullfil the need for fast and robust object detection, necessary in many applications in multiple domains (surveillance, traffic, image retrieval, &#8230;). The algorithm lends itself to be implemented in a parallel way. We exploit this [&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":[36,11,73,90,3],"tags":[39,1787,1782,1791,20,436,1793],"class_list":["post-8322","post","type-post","status-publish","format-standard","hentry","category-algorithms","category-computer-science","category-computer-vision","category-opencl","category-paper","tag-algorithm-optimization","tag-algorithms","tag-computer-science","tag-computer-vision","tag-nvidia","tag-nvidia-geforce-gtx-295","tag-opencl"],"views":2623,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/8322","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=8322"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/8322\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=8322"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=8322"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=8322"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}