Academic Lecture | Coordinated 3D World Exploration of Big Visual Data for Smart City and Autonomous Driving Applications


Published: 2019.12.17

TitleCoordinated 3D world exploration of big visual data for smart city and autonomous driving applications

Time: 13:00 on December 18, 2019

Location:Information Building 133

Speaker: Prof. Jenq-Neng Hwang

Moderator: Li Qingli

Introduction of the speaker:

Dr. Jenq-Neng Hwang received the BS and MS degrees, both in electrical engineering from the National Taiwan University, Taipei, Taiwan, in 1981 and 1983 separately. He then received his Ph.D. degree from the University of Southern California. In the summer of 1989, Dr. Hwang joined the Department of Electrical and Computer Engineering (ECE) of the University of Washington in Seattle, where he has been promoted to Full Professor since 1999. He served as the Associate Chair for Research from 2003 to 2005, and from 2011-2015. He is currently the Associate Chair for Global Affairs and International Development in the ECE Department. He is the founder and co-director of the Information Processing Lab., which has won CVPR AI City Challenges awards consecutively in the past years. He has written more than 350 journal, conference papers and book chapters in the areas of machine learning, multimedia signal processing, and multimedia system integration and networking, including an authored textbook on Multimedia Networking: from Theory to Practice, published by Cambridge University Press. Dr. Hwang has close working relationship with the industry on multimedia signal processing and multimedia networking.

Dr. Hwang received the 1995 IEEE Signal Processing Society's Best Journal Paper Award. He is a founding member of Multimedia Signal Processing Technical Committee of IEEE Signal Processing Society and was the Society's representative to IEEE Neural Network Council from 1996 to 2000. He is currently a member of Multimedia Technical Committee (MMTC) of IEEE Communication Society and also a member of Multimedia Signal Processing Technical Committee (MMSP TC) of IEEE Signal Processing Society. He served as associate editors for IEEE T-SP, T-NN and T-CSVT, T-IP and Signal Processing Magazine (SPM). He is currently on the editorial board of ZTE Communications, ETRI, IJDMB and JSPS journals. He served as the Program Co-Chair of IEEE ICME 2016 and was the Program Co-Chairs of ICASSP 1998 and ISCAS 2009. Dr. Hwang is a fellow of IEEE since 2001.

Lecture summary:

With the huge amount of networked static surveillance and moving video cameras available everywhere nowadays, such as the cameras on the vehicles/drone for autonomous driving or aerial surveillance applications, there is an urgent need of systematic and coordinated mining of the detected video objects in the 3D physical world, so that the explored information can be exploited for various smart city applications. To achieve this goal, several critical challenges need to be effectively overcome, more specifically, reliable SLAM-based visual odometry for pose estimation (self-calibration) of moving cameras, robust tracking-by-detection and detection-by-tracking for detected object associations in presence of missing or erroneous detections, reliable ground plane estimation for 2D to 3D inferences, finally efficient 3D pose estimation for action description of detected human. In this talk, I will cover all these topics and propose our optimized strategies of integrating these research components, practical applications for smart city and autonomous driving will be demonstrated.