Monte Carlo Markov Chain Methods for Video Understanding

Abstract: Recent advances in Monte Carlo Markov Chain (MCMC) methods have enabled the design of elegant algorithms for many problems related to the understanding of video images. In this talk, I will summarize some of our recent work in this area. Specifically, I will present  MCMC techniques for tracking humans using their motion, shape and identification. Algorithms for recovering the structure of multiple moving objects will also be presented. Some open issues on critical motions sequences that arise in self-calibration will be discussed and a Bayesian approach for self-calibration is presented. Many illustrative video  examples will be shown.

Biography: Prof. Rama Chellappa received his Ph.D. degree from Purdue University in 1981. During 1981-1991, he was an assistant and associate professor at University of Southern California and served as the Director of Signal and Image Processing Institute during 1988-1990. Since 1991, he has been a Professor of Electrical and Computer Engineering Department and an affiliate Professor of Computer Science Department at  University of Maryland. In July 2001, he became the Director of Center for Automation Research as well as a Permanent member of the Institute for Advanced Computer Studies. He has published numerous papers in image processing, analysis and understanding. Professor Chellappa has received several awards including the NSF PYI Award, the IBM Faculty Development Award, Excellence in Teaching Award, the IEEE Signal Processing Society Technical Achievement Award and the Distinguished Faculty Research Fellow Award. He is the EIC of the IEEE Transactions on Pattern Analysis and Machine Intelligence and the Vice President of IEEE Signal Processing Society in charge of Awards and Membership.