Computer vision in transportation has recently received increasing attention from both industry and academia due to the popularity of modern mobile transportation platforms and the rapid development of autonomous driving. In this tutorial, we systematically introduce the recent progresses of computer vision techniques and their applications in transportation. Specifically, we will provide a general overview of the key problems, common formulations, existing methodologies and future directions. This tutorial will inspire the audience and facilitate research in computer vision for transportation.
The tutorial mainly consists of three parts:
Haifeng SHEN, DiDi AI Labs, Didi Chuxing
Zhengping CHE, DiDi AI Labs, Didi Chuxing
Guangyu LI, DiDi AI Labs, Didi Chuxing
Yuhong GUO, DiDi AI Labs, Didi Chuxing & Carleton University
Jieping YE, DiDi AI Labs, Didi Chuxing & University of Michigan, Ann Arbor
Yiping MENG, Research Outreach, Didi Chuxing
Haifeng Shen is a senior expert algorithm engineer in Didi Chuxing and leads the computer vision group in the AI Labs. He received his Ph.D. degree in signal and information processing from Beijing University of Posts and Telecommunications, in 2006. He has worked at Panasonic, Baidu, and Microsoft. He built the first speech recognition interface for XiaoIce chatbot in Microsoft and his current work focuses on computer vision in transportation. In 2019, he led the computer vision group to attend worldwide wider face detection evaluation and won 5 rank-1 honors. His research interests include computer vision, speech recognition and natural language processing.
Zhengping Che is a senior research scientist at DiDi AI Labs. He received his Ph.D. in Computer Science from the University of Southern California. Before that, he received his B.E. in Computer Science from Pilot CS Class (Yao Class), Tsinghua University. His current research interests lie in machine learning, deep learning and data mining with applications to temporal data and vision data. He has published several papers in ICML, KDD, ICDM, AMIA and other venues and interned at DiDi AI Labs, Mayo Clinic, IBM Research, Google and Hulu.
Guangyu Li is a senior research scientist at DiDi AI Labs. In this role, he works on intelligent vehicles regarding autonomous driving, intelligent cockpit, and IoT systems. Before that, he developed perception algorithms for self-driving trucks at TuSimple, an autonomous truck unicorn. Besides industrial experience, he is also a PhD candidate in University of Southern California. His research interests lie in computer vision, large scale sensor systems, and virtual/augmented/mixed reality with a focus on their applications in modern intelligent transportation.
Yuhong Guo is a principal research scientist at Didi Chuxing. She is also an associate professor at Carleton University, a faculty affiliate of the Vector Institute, and a Canada Research Chair in Machine Learning. She received her PhD from the University of Alberta, and has previously worked at the Australian National University and Temple University. Her research interests include machine learning, artificial intelligence, computer vision, and natural language processing. She has won paper awards from both IJCAI and AAAI. She has served in the Senior Program Committees of AAAI, IJCAI and ACML, and is currently serving as an Associate Editor for TPAMI.
Jieping Ye is head of DiDi AI Labs, a VP of Didi Chuxing. He is also a professor of University of Michigan, Ann Arbor. His research interests include big data, machine learning, and data mining with applications in transportation and biomedicine. He has served as a Senior Program Committee/Area Chair/Program Committee Vice Chair of many conferences including NIPS, ICML, KDD, IJCAI, ICDM, and SDM. He has served as an Associate Editor of Data Mining and Knowledge Discovery, IEEE Transactions on Knowledge and Data Engineering, and IEEE Transactions on Pattern Analysis and Machine Intelligence. He won the NSF CAREER Award in 2010. His papers have been selected for the outstanding student paper at ICML in 2004, the KDD best research paper runner up in 2013, and the KDD best student paper award in 2014.
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