{"id":75,"date":"2017-08-09T16:31:51","date_gmt":"2017-08-09T16:31:51","guid":{"rendered":"http:\/\/wp.ee.washington.edu\/cmmb\/?p=75"},"modified":"2017-08-09T16:31:51","modified_gmt":"2017-08-09T16:31:51","slug":"uw-ee-based-team-wins-ai-city-challenge","status":"publish","type":"post","link":"https:\/\/wp.ece.uw.edu\/cmmb\/2017\/08\/09\/uw-ee-based-team-wins-ai-city-challenge\/","title":{"rendered":"UW EE-based team wins AI City Challenge"},"content":{"rendered":"<div id=\"attachment_76\" style=\"width: 300px\" class=\"wp-caption alignleft\"><img loading=\"lazy\" decoding=\"async\" aria-describedby=\"caption-attachment-76\" class=\"size-medium wp-image-76\" src=\"https:\/\/ada.ece.uw.edu\/wp-content\/uploads\/sites\/34\/2017\/08\/2017-08_AI-City-Challenge-300x225.jpg\" alt=\"Jenq-Neng Hwang and the team\" width=\"300\" height=\"225\" srcset=\"https:\/\/wp.ece.uw.edu\/wp-content\/uploads\/sites\/34\/2017\/08\/2017-08_AI-City-Challenge-300x225.jpg 300w, https:\/\/wp.ece.uw.edu\/wp-content\/uploads\/sites\/34\/2017\/08\/2017-08_AI-City-Challenge-768x576.jpg 768w, https:\/\/wp.ece.uw.edu\/wp-content\/uploads\/sites\/34\/2017\/08\/2017-08_AI-City-Challenge-750x563.jpg 750w, https:\/\/wp.ece.uw.edu\/wp-content\/uploads\/sites\/34\/2017\/08\/2017-08_AI-City-Challenge.jpg 1024w\" sizes=\"auto, (max-width: 300px) 100vw, 300px\" \/><p id=\"caption-attachment-76\" class=\"wp-caption-text\">The UW EE team at the AI City Challenge.<\/p><\/div>\n<p>A team of graduate students and researchers, led by UW Electrical Engineering (UW EE) Professor <a href=\"http:\/\/www.ee.washington.edu\/people\/jenq-neng-hwang\/\">Jenq-Neng Hwang<\/a>\u00a0won the Track 2 challenge in the\u00a0<a href=\"http:\/\/smart-city-conference.com\/AICityChallenge\/\">IEEE Smart World NVIDIA AI City Challenge<\/a>.<\/p>\n<p>Estimations assert that there will be 1 billion cameras on the road by 2020. These cameras present\u00a0a significant opportunity for transportation; their data can offer actionable insights to make transportation systems smarter and safer. However, current systems present challenges. Poor data quality, a lack of labels for the data and the lack of high quality models that can convert the data into actionable insights are some of the biggest\u00a0blockers to harnessing the data&#8217;s value.<\/p>\n<p>The\u00a0AI City Challenge requested research proposals for two tracks to\u00a0address these problems. For Track 1, researchers were asked to develop models for basic machine learning tasks as they applied to current transportation systems&#8217; data. Track 2 researchers were asked to propose and develop AI City applications geared towards solving salient problems related to safety and\/or congestion in an urban environment.<\/p>\n<p>The team of researchers in Professor Hwang&#8217;s <a href=\"http:\/\/allison.ee.washington.edu\/index.htm\">Information Processing Lab<\/a>\u00a0won the Track 2 challenge for AI City Applications. \u00a0Their work focused on a\u00a0constrained multiple-kernel (CMK) tracking system to resolve the problem of occlusion during multiple object tracking. This model also enables researchers to understand\u00a0vehicle attributes, like vehicle type, speed, orientation, etc. The researchers also\u00a0proposed several future improvements on their current work, including license plate identification and application in multiple-camera tracking. Their experiments on the NVIDIA dataset outperformed\u00a0several state-of-the-art algorithms in tracking by segmentation and tracking by detection.<\/p>\n<p>The\u00a0AI City Challenge is jointly sponsored by IEEE and NVIDIA\u00a0through the <a href=\"http:\/\/ieee-smartworld.org\/2017\/smartworld\/\">IEEE Smart World Congress<\/a> annual conference. Researchers for Track 2 included\u00a0Zheng (Thomas) Tang,\u00a0Gaoang Wang,\u00a0Tao Liu,\u00a0Young-Gun Lee,\u00a0Adwin Jahn,\u00a0Xu Liu,\u00a0Dr. Xiaodong He from Microsoft Research and\u00a0Professor Hwang.<\/p>\n<p>Team lead Zheng Tang urged that this win was a team effort, thanking the UW undergraduate students, who offered assistance:\u00a0Lingli Zeng,\u00a0Aotian Zheng,\u00a0Yan Kuo,\u00a0Kevin Nguyen,\u00a0Jingwen Sun and\u00a0Chien-Jen Hwang.\t\t<\/p>\n","protected":false},"excerpt":{"rendered":"<p>\t\t\t\tA team of graduate students and researchers, led by UW Electrical Engineering Professor Jenq-Neng Hwang, won the Track 2 challenge in the IEEE Smart World NVIDIA AI City Challenge.\t\t<\/p>\n","protected":false},"author":23,"featured_media":134,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"inline_featured_image":false,"footnotes":""},"categories":[1],"tags":[],"class_list":["post-75","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-uncategorized","item-wrap"],"_links":{"self":[{"href":"https:\/\/wp.ece.uw.edu\/cmmb\/wp-json\/wp\/v2\/posts\/75","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/wp.ece.uw.edu\/cmmb\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/wp.ece.uw.edu\/cmmb\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/wp.ece.uw.edu\/cmmb\/wp-json\/wp\/v2\/users\/23"}],"replies":[{"embeddable":true,"href":"https:\/\/wp.ece.uw.edu\/cmmb\/wp-json\/wp\/v2\/comments?post=75"}],"version-history":[{"count":0,"href":"https:\/\/wp.ece.uw.edu\/cmmb\/wp-json\/wp\/v2\/posts\/75\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/wp.ece.uw.edu\/cmmb\/wp-json\/wp\/v2\/media\/134"}],"wp:attachment":[{"href":"https:\/\/wp.ece.uw.edu\/cmmb\/wp-json\/wp\/v2\/media?parent=75"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/wp.ece.uw.edu\/cmmb\/wp-json\/wp\/v2\/categories?post=75"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/wp.ece.uw.edu\/cmmb\/wp-json\/wp\/v2\/tags?post=75"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}