主題報告： Vehicle Model Recognition based on Deep Network
報告專家：韓國全北國立大學Hyo Jong Lee教授
報告時間：6月20日（周二）下午14:50
報告地點：教學樓D211
主講人介紹🏄🏿♀️：
Hyo Jong Lee教授本科👨🚀，碩士與博士均畢業於美國猶他大學，取得了氣象學專業與計算機專業雙博士學位，自1991年起在韓國全北國立大學擁有26年的教學經驗，期間擔任系主任以及高級圖像與信息技術中心主任。與此同時，李教授曾以訪問教授在英國布裏斯托爾大學從事研究工作，並長期（7年）在美國加州大學兼職工作，擁有非常高的全英文教學水平。另一方面，李教授在圖像處理，模式識別與並行計算中有豐富的科研經驗，總共發表70多篇高水平期刊論文，120多篇會議論文。李教授承擔過韓國國家研究基金(NRF)⛑，韓國科技部(MSIP),韓國產學研項目等十多項科研課題。
講座內容簡介：
In recent years, vehicle recognition has become an important application in intelligent traffic monitoring and other vehicle management. Vehicle analysis is an essential component in many intelligent applications, such as automatic toll collection, driver assistance systems, self-guided vehicles, intelligent parking systems, and traffic statistics (vehicle count, speed, and flow). In this talk our method, which extract vehicle information from the moving vehicles like their make, model and type, is presented. We address the detection of moving vehicles and recognition problems using Deep Neural Networks (DNNs) approach. A large scale vehicle dataset (~300,000) was collected and labelled from the street cameras, The accuracy of our algorithm is 96.3% and it achieves promising results on our actual data.
