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    Coordinated 3D Physical World Exploration from Big Visual Data

    2018-12-21來源: 瀏覽次數:

    題目:Coordinated 3D Physical World Exploration from Big Visual Data

    報告時間:2018年12月24日(星期一) 下午15點


    報告人:Prof. Jenq-Neng Hwang



    There are huge amount of visual data collected everywhere nowadays, either from the statically deployed surveillance cameras or the constantly moving cameras on the vehicles or drones. Built upon the success of various deep learning architectures for image-based object detection, segmentation and 2D pose estimation, there is an urgent need of systematic and coordinated mining of the dynamic environment in the 3D physical world, so that the explored information can be exploited for various smart city applications, such as security surveillance, intelligent transportation, business statistics collection, health monitoring of communities, and etc. In this talk, I will first present an automated and robust human/vehicle tracking and 3D localization through self-calibration of static and moving monocular cameras. The 3D locations and speeds of these tracked objects, as well as the poses of tracked humans, can all be described based on the GPS coordinates, so that the tracked objects from multiple cameras can then be effectively integrated and reconstructed in the 3D real-world space for many smart city and intelligent transportation applications.


    Dr. Jenq-Neng Hwang received the BS and MS degrees, both in electrical engineering from the 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 several AI City Challenges awards in the past years. He has written more than 330 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.

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