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.

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. His 2001 paper with Allan Jepson and Thomas El-Maraghi on robust appearance models for visual tracking was awarded runner-up for the best paper at the IEEE Conference on Computer Vision and Pattern Recognition. In 2003, his paper with Eric Saund, James Mahoney and Dan Larner won the best paper award at ACM UIST '03. He has been associate editor of IEEE Transactions on Pattern Analysis and Machine Intelligence (2000-2004), and program co-chair for the IEEE Conference on Computer Vision and Pattern Recognition in 2003. He is currently Associate Editor-In-Chief for IEEE Transactions on Pattern Analysis and Machine Intelligence, and a 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, 3D people tracking, modeling appearance in image sequences, non-Fourier motion and stereo perception, and the neural basis of stereo vision.

Abstract:

Today most common methods for accurate capture of three-dimensional human movement require a laboratory environment and the attachment of markers or fixtures to the body's segments. These laboratory conditions can cause
unknown experimental artifacts. Modern biomechanical and clinical applications require the accurate capture of normal and pathological human movement without the artifacts associated with standard marker-based motion capture techniques such as soft tissue artifacts and the risk of artificial stimulus of taped-on or strapped-on markers. The need for accurate
information on the characteristics of normal and pathological human is motivated in part by the introduction of new clinical approaches for the treatment and prevention of diseases that are influenced by subtle changes in the patterns movement. The need for markerless human motion capture methods is discussed and the advancement of markerless approaches is
considered in view of accurate capture of three-dimensional human movement for biomechanical applications. The role of choosing appropriate technical equipment and algorithms for accurate markerless motion capture is critical. The implementation of this new methodology offers the promise for simple, time-efficient, and potentially more meaningful assessments of human movement in research and clinical practice. The feasibility of accurately and precisely measuring 3D human body kinematics using a markerless motion capture system is demonstrated.

Bio: The primary mission of the Biomotion Research Group is to study normal and pathological function which can be ultimately applied to the improved evaluation and treatment of musculoskeletal disease and injury. The goals are addressed by studying normal subjects and patients with injury or disease that influence the normal function of the musculoskeletal system. In addition, the BioMotion Research Group is also committed to the development of improved methods for the measurement and analysis of human movement and is working on a markerless system using multiple optical sensors that will efficiently and accurately provide 3D measurements of human movement for application in clinical practice.