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Talks & Seminars
Human Movement and Activity Analysis - Beyond Trajectories
Prof. Larry S. Davis, Computer Science Department, University of Maryland
Date & Time: January 25, 2008 14:15
Venue: Conference Room, ā€˜Cā€™ Block, 01st floor, Kanwal Rekhi Building
Abstract:
Research on human movement and activity analysis in computer vision has emphasized the recovery of trajectories -of body parts for movement analysis and of people through the world for activity analysis. In this talk, I will discuss projects at the University of Maryland that look "beyond" trajectories for movement and activity analysis.

Humans are highly adept at executing movements associated with everyday activities. The movements are fast and efficient. The high speeds result in impulsive propulsion, with characteristic acceleration and deceleration phases. Such movements are called "Ballistic" due to their impulsive nature, and include reaches and strikes. These movements are most commonly used for interacting with objects and the environment. We describe a Bayesian approach for visual analysis of ballistic hand movements. One of the key challenges to recognizing them is the variability of the target-location of the hand~- people can reach above their heads, for something on the floor, etc. Our approach recognizes them independent of the movement's target-location and direction by modeling the ballistic dynamics. A video sequence is automatically segmented into ballistic subsequences without tracking the hands. The segments are then classified into strike and reach movements based on low-level motion features. Each ballistic segment is further analyzed to compute qualitative labels for the movement's target-location and direction. Tests are presented with a set of reach and strike movement sequences.

Visibility in architectural layouts affects human navigation, so a suitable representation of visibility context is useful in understanding human activity. Motivated by studies of spatial behavior, we use a set of features from visibility analysis to represent spatial context in the interpretation of human activity. An agent's goal, belief about the world, trajectory and visible layout are considered to be random variables that evolve with time during the agent's movement, and are modeled in a Bayesian framework. We design a search-based task in a sprite-world, and compare the results of our framework to those of human subject experiments. Our findings confirm that knowledge of spatial layout improves human interpretations of the trajectories (implying that visibility context is useful in this task). Since our framework demonstrates performance close to that of human subjects with knowledge of spatial layout, our findings confirm that our model makes adequate use of visibility context. In addition, the representation we use for visibility context allows our model to generalize well when presented with new scenes.

Speaker Profile:
Larry S. Davis received his B.A. from Colgate University in 1970 and his M. S. and Ph. D. in Computer Science from the University of Maryland in 1974 and 1976 respectively. From 1977-1981 he was an Assistant Professor in the Department of Computer Science at the University of Texas, Austin. He returned to the University of Maryland as an Associate Professor in 1981. From 1985-1994 he was the Director of the University of Maryland Institute for Advanced Computer Studies. He is currently a Professor in the Institute and the Computer Science Department, as well as Chair of the Computer Science Department. He was named a Fellow of the IEEE in 1997.

Prof. Davis is known for his research in computer vision and high performance computing. He has published over 100 papers in journals and has supervised over 20 Ph. D. students. He is an Associate Editor of the International Journal of Computer Vision and an area editor for Computer Models for Image Processing: Image Understanding. He has served as program or general chair for most of the field's major conferences and workshops, including the 5'th International Conference on Computer Vision, the 2004 Computer Vision and Pattern Recognition Conference, and the 11'th International Conference on Computer Vision.

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