Topic：High-speed Vision Tracking with Deep Intelligence
Speaker：Prof. Idaku Ishii (Hiroshima University, Japan)
Time：10:30am -12:00am, March 13
Venue：E706, New Main Building
Abstract：
High-speed vision system that can acquire and process images or videos at a high rate (hundreds or thousands frames per second) has significant advantages over standard video cameras operating at dozens of frames per second in areas of tracking, robot control, industrial manufacturing, navigation and motion analysis and so on. The main challenge to achieving high-speed performance is how to manage and coordinate image processing and system controlling in a extremely short period of time. In this talk, our developed high-speed vision systems with intelligent tracking are introduced including: 1) high-speed multithread active vision with ultrafast mirror-drive devices, 2) super-telephoto detecting and tracking with deep learning, and 3) vibration source tracking with pixel-level high-speed vision signal processing. These proposed systems show how high-speed vision can combine ultrafast optomechatronic devices and deep intelligent processing for real-world applications such as high-speed targets detecting, tracking and zooming at a distance.
Biography of the Speaker:
Prof. Idaku Ishii received the B.E., M.E., and Ph.D. degrees from the University of Tokyo, Japan, in 1992, 1994, and 2000, respectively. He is currently a Professor with the Graduate School of Engineering, Hiroshima University, Japan. His current research interests include high-speed vision, sensory based processing and robot manipulation, high-speed tracking and surveillance with deep intelligence, and applications in industry and biomedicine. He has published two academic books and over 200 journal and international conference papers. He also held over dozens of Japanese and international patents. His lab has obtained several major research projects from government, universities or institutions as well as enterprises in Japan and overseas, and got large amount of research funding support.
School of Automation Science and Electrical Engineering
