Ahmed M. Elgammal

The role of Manifold learning in Human Motion Analysis

Abstract

Human body is an articulated object with high degrees of freedom. Despite the high dimensionality of the configuration space, many human motion activities lie intrinsically on low dimensional manifolds. Although the intrinsic body configuration manifolds might be very low in dimensionality, the resulting appearance manifolds are challenging to model given various aspects that affects the appearance such as the shape and appearance of the person performing the motion, or variation in the view point, or illumination. Our objective is to learn representations for the shape and the appearance of moving (dynamic) objects that support tasks such as synthesis, pose recovery, reconstruction, and tracking. We studied various approaches for representing global deformation manifolds that preserve their geometric structure. Given such representations, we can learn generative models for dynamic shape and appearance. We also address the fundamental question of separating style and content on nonlinear manifolds representing dynamic objects.We learn factorized generative models that explicitly decompose the intrinsic body configuration (content) as a function of time from the appearance/shape (style factors) of the person performing the action as time-invariant parameters. We show results on pose recovery, body tracking, gait recognition, as well as facial expression tracking and recognition.

Bio

Dr. Ahmed Elgammal is an assistant professor at the Department of Computer Science, Rutgers, the State University of New Jersey Since Fall 2002. Dr. Elgammal is also a member of the Center for Computational Biomedicine Imaging and Modeling (CBIM) and the Center for Advanced Information Processing (CAIP) at Rutgers. His primary research interest is computer vision and machine learning. His research focus includes human activity recognition, human motion analysis, tracking, human identification, and statistical methods for computer vision. He develops robust real-time algorithms to solve computer vision problems in areas such as visual surveillance, visual human-computer interaction, virtual reality, and multimedia applications. Dr. Elgammal interest includes also research on document image analysis. Dr. Elgammal received the National Science Foundation early CAREER Award in 2006.

Dr. Elgammal received his B.Sc. and M.Sc. degrees in computer science and automatic control from University of Alexandria, Egypt in 1993 and 1996, respectively. He received another M.Sc. and his Ph.D. degree in computer science from the University of Maryland, College Park, in 2000 and 2002 respectively.