2010 Poster Sessions : Efficient Extraction of Human Motion Volumes by Tracking

Student Name : Juan Carlos Niebles
Advisor : Fei-Fei Li
Research Areas: Artificial Intelligence
We present an automatic and efficient method to extract spatio-temporal human volumes from video, which combines top-down model-based and bottom-up appearance based approaches. From the top-down perspective, our algorithm applies shape priors probabilistically to candidate image regions obtained by pedestrian detection, and provides accurate estimates of the human body areas which serve as important constraints for bottom-up processing. Temporal propagation of the identified region is performed with bottom-up cues in an efficient level-set framework, which takes advantage of the sparse top-down information that is available. Our formulation also optimizes the extracted human volume across frames through belief propagation and provides temporally coherent human regions. We demonstrate the ability of our method to extract human body regions efficiently and automatically from a large, challenging dataset collected from YouTube.

Juan Carlos Niebles is a visiting researcher in the Computer Science Department at Stanford University and a Ph.D. student in the Electrical Engineering Department at Princeton University, working under the advise of Prof. Fei-Fei Li. He received a M.A. degree in Electrical Engineering from Princeton University in 2009, a M.Sc. degree in Electrical Engineering from University of Illinois in 2007 and a Electronic Engineer degree from Universidad del Norte (Colombia) in 2002. He received a Fulbright Fellowship for graduate studies in 2005.