Dr. Rama Chellappa, Professor of Electrical and Computer Engineering
University of Maryland
With the ubiquitous presence of inexpensive video cameras, new challenges to video-based pattern recognition problems are emerging. Video-based pattern recognition problems have applications in homeland security, healthcare, video indexing and anomaly detection. The single most important feature that distinguishes video-based pattern recognition problems from still-image based recognition problems is the dynamical nature of patters in videos. This creates new intellectual challenges and provides opportunities for novel approaches.
In this talk, I will discuss some of the general principles for designing robust video-based pattern recognition systems using statistical methods. I will then discuss the design of statistical parametric classifiers for video-based face recognition and recognition of bee dances using appearance, shape and behavior encoded particle filters. Algorithms for gait-based human identification and human activity recognition/anomaly detection using statistical inference on manifolds will then be presented. Methods for addressing the variations due to time warping and viewpoints will be illustrated for unsupervised clustering of video sequences. Finally, we will discuss some theoretical issues and practical problems that remain to be addressed in this area.
Professor Chellappa received the M.S.E.E. and Ph.D. Degrees in Electrical Engineering from Purdue University, West Lafayette, IN, in 1978 and 1981 respectively. Since 1991, he has been a Professor of Electrical and Computer Engineering and an affiliate professor of Computer Science at the University of Maryland (UMD), College Park. He is also affiliated with the Center for Automation Research (Director) and the Institute for Advanced Computer Studies (Permanent member). Recently, he was named a Minta Martin Professor of Engineering. Over the past 26 years, he has published numerous book chapters, peer-reviewed journal and conference papers. He has co-authored and edited many books in visual surveillance, biometrics, MRFs and image processing. His current research interests are in face and gait analysis, 3D modeling from video, surveillance and monitoring, hyper spectral processing, and computer vision. Professor Chellappa served as the associate editor of four IEEE Transactions and as the Editor-in-Chief of IEEE Transactions on Pattern Analysis and Machine Intelligence. He served as a member of the IEEE Signal Processing Society’s Board of governors and as its Vice President of Awards and Membership. He has also served as a General and Technical Program Chair/Co-Chair for several IEEE international, national conferences and workshops.