[Colloquium] TTI-C Talk: Leonid Sigal, University of Toronto

[Julia MacGlashan]

REMINDER

When:             Thursday, March 19th @ 11:00am (lunch will be provided
after talk)

Where:            6045 S Kenwood Ave, TTI-C Conference Room #526 (5th Floor)

Who:               Leonid Sigal, University of Toronto

Title:                Physics-based Models and Priors for Human Motion
Tracking


Recovery and analysis of human pose and motion from video is the key
enabling technology for a broad spectrum of applications, in and outside of
computer science; including applications in HCI, biometrics, biomechanics
and computer graphics. Despite years of research, the general problem of
tracking a person in an unconstrained environment, particularly from
monocular observations, remains challenging. In this talk I will describe
the basic building blocks and challenges of the articulated human pose
estimation and tracking, as well as my contributions to the various aspects
of this problem and the field in recent years. I will particularly focus on
the new and unique class of models that incorporate physic-based predictions
and simulation into the inference process. Physics plays an important and
intricate role in characterizing, describing and predicting human motion.
The key benefit of using physics-based models or priors for tracking is the
improved realism in the recovered motions, as well as enhanced ability to
deal with weak image observations and diverse environmental interactions.
Newtonian physics, in these models, approximates the rigid-body dynamics of
the body in the environment through the application and integration of
forces. Since the motion of the body is intimately tied with the
environment, the use of such models also allows one to start reasoning about
the geometry and physical properties of the environment as a whole (e.g.
orientation and compliance of ground). This work is part of joint projects
with colleagues at Brown University and University of Toronto.

Bio:

Leonid Sigal is a postdoctoral fellow in the Department of Computer Science
at University of Toronto. He received his Ph.D. in computer science from
Brown University (2007); his M.S. from Brown University (2003); his M.A.
from Boston University (1999); and his B.Sc. degrees in Computer Science and
Mathematics from Boston University (1999). From 1999 to 2001, he worked as a
senior vision engineer at Cognex Corporation, where he developed industrial
vision applications for pattern analysis and verification. In 2002, he spent
a semester as a research intern at Siemens Corporate Research (SCR) working
on autonomous obstacle detection and avoidance for vehicle navigation.
During the summers of 2005 and 2006, he worked as a research intern at Intel
Applications Research Lab (ARL) on human pose estimation and tracking. His
work received the Best Paper Award at the Articulate Motion and Deformable
Objects Conference in 2006 (with Prof. Michael J. Black). Dr. Sigal's
research interests mainly lie in the areas of computer vision and machine
learning, but also borderline fields of computer graphics, psychology and
humanoid robotics. He is particularly interested in statistical models for
problems of visual inference, including human motion recovery and analysis,
graphical models, probabilistic and hierarchical inference. 

Contact:          Greg Shakhnarovich, TTI-C		greg at tti-c.org
834-2572




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