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Ontario Tech acknowledges the lands and people of the Mississaugas of Scugog Island First Nation.

We are thankful to be welcome on these lands in friendship. The lands we are situated on are covered by the Williams Treaties and are the traditional territory of the Mississaugas, a branch of the greater Anishinaabeg Nation, including Algonquin, Ojibway, Odawa and Pottawatomi. These lands remain home to many Indigenous nations and peoples.

We acknowledge this land out of respect for the Indigenous nations who have cared for Turtle Island, also called North America, from before the arrival of settler peoples until this day. Most importantly, we acknowledge that the history of these lands has been tainted by poor treatment and a lack of friendship with the First Nations who call them home.

This history is something we are all affected by because we are all treaty people in Canada. We all have a shared history to reflect on, and each of us is affected by this history in different ways. Our past defines our present, but if we move forward as friends and allies, then it does not have to define our future.

Learn more about Indigenous Education and Cultural Services

September 12, 2012

Speaker: Marcus Brubaker, PhD Postdoctoral Researcher, Toyota Technological Institute at Chicago

Title: Physics in Human Motion Estimation and Scene Understanding

Abstract: Recent work has demonstrated that a physically realistic model of the world can greatly aid in estimating human motion from video. While improved tracking and estimation methods are one natural application of physics based models of human motion there are other interesting applications. This talk will present two applications of physics-based models of human motion to the broader context of dynamic scene understanding. From motion alone we are able to recover scene properties, such as the position of a ground plane and the orientation of gravity. We are able to do this using both noisy tracking results and motion capture data. Further, when given noisy motions such as those typically generated by modern tracking algorithms, we are able to restore a higher quality, physically realistic motion. When data is missing (such as entire frames or body parts which were not tracked) we are able to recover this missing data in a physically plausible manner.

Biography: Marcus Brubaker received his BSc, MSc and PhD in Computer Science from the University of Toronto. His graduate work was performed under the supervision of David Fleet. Since 2011 he has been a postdoctoral scholar at Toyota Technological Institute at Chicago and has worked jointly at the University of Toronto and, currently, the Toronto Rehabilitation Institute. He also consults with Cadre Research Labs on machine learning and computer vision related projects.

He has worked on video-based human motion estimation, physical models of human motion, video-based tracking, Markov Chain Monte Carlo methods, ballistic forensics, electron cryo-microscopy, time series modelling and automatic vehicle localization. His interests span computer vision, computer graphics, machine learning and statistics.