{"id":13725,"date":"2015-03-12T00:12:00","date_gmt":"2015-03-11T22:12:00","guid":{"rendered":"http:\/\/hgpu.org\/?p=13725"},"modified":"2015-03-12T00:12:00","modified_gmt":"2015-03-11T22:12:00","slug":"implementing-machine-learning-algorithms-on-gpus-for-real-time-traffic-sign-classification","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=13725","title":{"rendered":"Implementing Machine Learning Algorithms on GPUs for Real-Time Traffic Sign Classification"},"content":{"rendered":"<p>This paper investigates traffic sign classification, which is an important problem to solve for autonomous driving. Linear discriminant analysis and convolutional neural networks achieved an accuracy of 98.25% and 98.75% respectively when classifying eight different types of traffic signs. The CNN was implemented on a GPU for real-time traffic sign classification: testing time for the CNN on a GPU was 4 ms\/image, which was 7.5x as fast as running LDA on a CPU and 60.2x as fast as running CNN on a CPU. Additionally, different types of classification errors and the effects of adding a new sign to the dataset were explored.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>This paper investigates traffic sign classification, which is an important problem to solve for autonomous driving. Linear discriminant analysis and convolutional neural networks achieved an accuracy of 98.25% and 98.75% respectively when classifying eight different types of traffic signs. The CNN was implemented on a GPU for real-time traffic sign classification: testing time for the [&hellip;]<\/p>\n","protected":false},"author":351,"featured_media":0,"comment_status":"open","ping_status":"closed","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":true,"jetpack_social_options":{"image_generator_settings":{"template":"highway","default_image_id":0,"font":"","enabled":false},"version":2}},"categories":[36,11,73,89,3],"tags":[1787,330,1782,1791,14,1025,34,20,1504],"class_list":["post-13725","post","type-post","status-publish","format-standard","hentry","category-algorithms","category-computer-science","category-computer-vision","category-nvidia-cuda","category-paper","tag-algorithms","tag-cnn","tag-computer-science","tag-computer-vision","tag-cuda","tag-machine-learning","tag-neural-networks","tag-nvidia","tag-nvidia-geforce-gtx-780"],"views":2292,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/13725","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=13725"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/13725\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=13725"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=13725"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=13725"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}