Prof. Reinhard Klette at Centre for Robotics & Vision at Auckland University of Technology, New Zealand
Towards Self-driving Cars: Test Fields, Visual Odometry, and Road-surface Analysis
Self-driving cars use various sensors, to be tested on extensive test fields under location, weather
In the context of test field studies, the talk informs about computer-vision based components towards self-driving cars. Visual odometry supports exact
Different camera installations contribute to options for sensor configurations in self-driving cars. The talk discusses results for visual odometry for evaluating different camera “topologies”. It also demonstrates how visual odometry provides important information for improved road-surface distress analysis, such as automated pothole detection.
Prof. Reinhard Klette is a professor in the EEE Department and in the Centre for Robotics & Vision at Auckland University of Technology, New Zealand
Professor Klette has been working in the area of computer vision for more than 30 years. In 2003 he published with the late Professor Azriel Rosenfeld of University of Maryland, USA, the first comprehensive monography on digital geometry (published by Morgan Kaufmann, San Francisco). He has become internationally renowned for his work in vision-based driver assistance since 2006, with important contributions on performance evaluation and improvements of correspondence algorithms on real-world video data, supporting, for example, 3D scene reconstruction from a mobile platform.
In 2008 he co-authored (with two of his former
Since 1995, Professor Klette has been invited as a keynote or plenary speaker to international conferences worldwide. Between April 2011 and October
Prof. Jiannong Cao at The Hong Kong Polytechnic University
Collaborative Task Execution in Advanced Edge Computing Environments
Advances in edge computing will boost more smart applications including AI models, AR, video analytics, and Industrial IoT applications. In an advanced edge computing environment, edge nodes of multiple stockholders are interconnected to facilitate the share of data and computation resources, and to collaborate on joint task execution using the shared resources. A fundamental issue is how to optimize the performance of collaborative task execution in terms of various metrics. In this talk, I will first introduce the major approaches to collaborative task execution, i.e., task partitioning, task allocation and task migration. In particular, I will present our recent work on multi-user multi-resource task partitioning, which is a new challenge and was not solved before. I will also highlight our works in data aware task allocation and task migration addressing the challenges arising from collaborative edge computing. Finally, I will conclude the talk by pointing out some future directions in this topic area.
Prof. Jiannong Cao is currently a Chair Professor of Department of Computing at The Hong Kong Polytechnic University, Hong Kong. He is also the director of the Internet and Mobile Computing Lab in the department and the director of University’s Research Facility in Big Data Analytics. His research interests include parallel and distributed computing, wireless sensing and networks, pervasive and mobile computing, and big data and cloud computing. He has co-authored 5 books, co-edited 9 books, and published over 600 papers in major international journals and conference proceedings. He received Best Paper Awards from conferences including, IEEE Trans. Industrial Informatics 2018, IEEE DSAA 2017, IEEE SMARTCOMP 2016, IEEE/IFIP EUC 2016, IEEE ISPA 2013, IEEE WCNC 2011, etc. Dr. Cao has directed and participated in over 90 research and development projects and, as a principal investigator, obtained over HK$43 million grants
Dr. Cao is currently a Chair Professor of Department of Computing at The Hong Kong Polytechnic University, Hong Kong. He is also the director of the Internet and Mobile Computing Lab in the department and the director of University’s Research Facility in Big Data Analytics. He served the department head from 2011 to 2017.
Dr. Cao’s research interests include parallel and distributed computing, wireless sensing and networks, pervasive and mobile computing, and big data and cloud computing. He has directed and participated in over 90 research and development projects and, as a principal investigator, obtained over HK$60 million grants from various funding agencies. He published 5 co-authored and 9 co-edited books, and over 500 papers in major international journals and conference proceedings. Dr. Cao also received Best Paper Awards from journals and conferences including IEEE TII, DSAA’2017, SMARTCOMP 2016, ISPA 2013, and IEEE WCNC 2011.
Dr. Cao served the Chair of the Technical Committee on Distributed Computing of IEEE Computer Society 2012-2014, and a member of many IEEE and other professional committees. He has served as chairs and members of organizing and technical committees of many international conferences, and as editors of many international journals. Dr. Cao is a fellow of IEEE and ACM distinguished member. In 2017, he received the Overseas Outstanding Contribution Award from China Computer Federation.