Title:
Physics-Based Models for Human Motion Analysis
Date:
Thursday, April 11, 2013 @ 1:00pm
Location:
E2-304 EITC Building, University of Manitoba Fort Garry Campus
Speaker:
Dr. David Fleet,
Department of Computer Science, University of Toronto
Abstract:
Future computer vision systems will spend a lot of time looking at people and interpreting their activities from digital video. Detecting and understanding people is considered by many to be a key enabling technology for myriad potential applications, including smart mobile devices, surveillance, man-machine interfaces, etc). This talk will outline recent research on modelling human pose and motion to help constrain video analysis. I will outline the problem of estimating 3D pose and motion, and recent advances in modelling human motion based on kinematic data, Newtonian principles, and bio-mechanical considerations.
This is joint work with Marcus Brubaker, Aaron Hertzmann, Leonid Sigal, and Jack Wang.
Speaker Bio:
David Fleet is Professor of Computer Science at the University of Toronto. He received the PhD in Computer Science from the University of Toronto in 1991. From 1991 to 2000 he was on faculty at Queen’s University, Canada, in the Department of Computing and Information Science, with cross-appointments in Psychology and Electrical Engineering. In 1999 he joined the Palo Alto Research Center (PARC) where he managed the Digital Video Analysis Group and the Perceptual Document Analysis Group. He returned to the University of Toronto in October 2003. He currently serves as Chair of the Department of
Computer and Mathematical Sciences, University of Toronto Scarborough.
In 1996 Dr. Fleet was awarded an Alfred P. Sloan Research Fellowship for his research on biological vision. His 1999 paper with Michael Black on probabilistic detection and tracking of motion boundaries received Honorable Mention for the Marr Prize at the IEEE International Conference on Computer Vision (ICCV). His 2001 paper with Allan Jepson and Thomas El-Maraghi on robust appearance models for visual tracking was awarded runner-up best paper at the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). In 2003, his paper with Eric Saund, James Mahoney and Dan Larner won the best paper award at ACM UIST ’03. With Francisco Estrada and Allan Jepson, he won the best paper award at the British Machine Vision Conference (BMVC) in 2009. In 2010, he was awarded the Koenderink Prize for his work with Michael Black and Hedvig Sidenbladh on human pose tracking.
He has served as Area Chair for numerous major computer vision and machine learning conferences. He was Associate Editor of IEEE Transactions on Pattern Analysis and Machine Intelligence (2000-2004), Program Co-Chair for the IEEE Conference on Computer Vision and Pattern Recognition in 2003, and Associate Editor-In-Chief for IEEE Transactions on Pattern Analysis and Machine Intelligence (2005-2008). He will be Program Co-Chair of ECCV 2014, and he currently serves on the Advisory Board for IEEE PAMI. He is Senior Fellow of the Canadian Institute of Advanced Research.
His research interests include computer vision, image processing, visual perception, and visual neuroscience. He has published research articles and one book on various topics including the estimation of optical flow and stereoscopic disparity, probabilistic methods in motion analysis, 2D visual tracking, 3D people tracking and hand tracking, modeling appearance in image sequences, physics-based models of human motion anlaysis, non-Fourier motion and stereo perception,
and the neural basis of stereo vision.
Cost:
This will be a free event.
Contact:
If you would like additional information or if you might be interested in presenting a seminar, please contact Stephane Durocher or the Department of Computer Science.
