{"id":1915,"date":"2010-12-09T11:10:16","date_gmt":"2010-12-09T11:10:16","guid":{"rendered":"http:\/\/hgpu.org\/?p=1915"},"modified":"2010-12-09T11:10:16","modified_gmt":"2010-12-09T11:10:16","slug":"fpga-gpu-architecture-for-kernel-svm-pedestrian-detection","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=1915","title":{"rendered":"FPGA-GPU architecture for kernel SVM pedestrian detection"},"content":{"rendered":"<p>We present a real-time multi-sensor architecture for video-based pedestrian detection used within a road side unit for intersection assistance. The entire system is implemented on available PC hardware, combining a frame grabber board with embedded FPGA and a graphics card into a powerful processing network. Giving classification performance top priority, we use HOG descriptors with a Gaussian kernel support vector machine. In order to achieve real-time performance, we propose a hardware architecture that incorporates FPGA-based feature extraction and GPU-based classification. The FPGA-GPU pipeline is managed by a multi-core CPU that further performs sensor data fusion. Evaluation on the INRIA benchmark database and an experimental study on a real-world intersection using multi-spectral hypothesis generation confirm state-of-the-art classification and real-time performance.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>We present a real-time multi-sensor architecture for video-based pedestrian detection used within a road side unit for intersection assistance. The entire system is implemented on available PC hardware, combining a frame grabber board with embedded FPGA and a graphics card into a powerful processing network. Giving classification performance top priority, we use HOG descriptors with [&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":[73,33,3],"tags":[1791,377,1786,20,436],"class_list":["post-1915","post","type-post","status-publish","format-standard","hentry","category-computer-vision","category-image-processing","category-paper","tag-computer-vision","tag-fpga","tag-image-processing","tag-nvidia","tag-nvidia-geforce-gtx-295"],"views":2137,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/1915","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=1915"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/1915\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=1915"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=1915"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=1915"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}