Prof. Mubarak Shah

Dr. Mubarak Shah, the UCF Trustee Chair Professor, is the founding director of Center for Research in Computer Visions at University of Central Florida (UCF). Dr. Shah is a fellow of IEEE, IAPR, AAAS and SPIE. With h-index of more than 100 and with total citations exceeding 50,000, he is one of the top Computer Science researchers in the world. He is a co-author of six books (Motion-Based Recognition (1997); Video Registration (2003); Automated Multi-Camera Surveillance: Algorithms and Practice (2008); Modeling, Simulation and Visual Analysis of Crowds (2013); and Robust Subspace Estimation Using Low-Rank Optimization (2014); Large-Scale Visual Geo-Localization (2016), all by Springer).  He has published extensively on topics related to visual surveillance, tracking, human activity and action recognition, object detection and categorization, shape from shading, geo registration, visual crowd analysis, etc.  He has been ACM and IEEE Distinguished Visitor Program speaker and is often invited to present seminars, tutorials and invited talks all over the world. He received Pegasus award in 2006; University Distinguished Research Award in 2017, 2012 and 2005; Faculty Excellence in Mentoring Doctoral Students in 2016, Scholarship of Teaching and Learning award in 2011; Teaching Incentive Program award in 1995 and 2003; Research Incentive Award in 2003, 2009 and 2012; the Harris Corporation Engineering Achievement Award in 1999; the TOKTEN awards from UNDP in 1995, 1997, and 2000; 2009 IEEE Outstanding Engineering Educator Award in 1997; an honorable mention for the ICCV 2005 Where Am I? Challenge Problem; 2013 NGA Best Research Poster Presentation, 2nd place in Grand Challenge at the ACM Multimedia 2013 conference; and runner up for the best paper award in ACM Multimedia Conference in 2005 and 2010

Title: View Invariant and Few Shot Human Action Recognition

Abstract
Automatic recognition of human actions from videos is one of the most active areas of research in Computer Vision.  My group has been working on this problem for some time and we have proposed several different methods addressing different aspects of this problem. Two important limitations of many of our approaches and other approaches proposed in the literature are: their sensitivity to view-point change and requirement of large number of training examples. In this talk I will present our recent work addressing both view-aware action recognition and learning action with less labels.