Computer Vision: Unmanned Aerial Vehicles and Video Processing

This event qualifies for 1 CPEs

Join us as ACM Fellow Dr. Larry Davis discusses an intro and history of Computer Vision, with real life applications such as unmanned aerial vehicles and wide field vehicle detection systems.

Agenda:

6:30 - Refreshments

7:00 - Announcements

7:15 - Lecture

8:15 - Q&A

8:45 - SIG-BEER Happy Hour

Abstract:

The field of computer vision was started in the 1960’s, in large part driven by applications in document image analysis (mail sorting). Fifty years later, there are many thousands of researchers and engineers around the world conducting fundamental and applied research in computer vision in applications areas ranging from astronomy to zoology. The talk will begin with a brief overview of the field from an applications perspective, highlighting some of the successes and open problems in a variety of application domains. Then, I will talk about two recent DARPA programs we have been involved in at the University of Maryland. The first, VIRAT, focused on human and vehicular activity recognition in UAV video. The second, Perseas, is concerned with detection and tracking of vehicles in very wide field of view video sensors.

Speaker and Presenter Information

Larry S. Davis received his B.A. from Colgate University in 1970 and his M. S. and Ph. D. in Computer Science from the University of Maryland in 1974 and 1976 respectively. From 1977-1981 he was an Assistant Professor in the Department of Computer Science at the University of Texas, Austin. He returned to the University of Maryland as an Associate Professor in 1981. From 1985-1994 he was the Director of the University of Maryland Institute for Advanced Computer Studies. He is currently a Professor in the Institute and the Computer Science Department. He was named a Fellow of the IEEE in 1997 and of the ACM in 2013. Prof. Davis is known for his research in computer vision and high performance computing. He has published over 100 papers in journals and 200 conference papers and has supervised over 25 Ph. D. students. During the past ten years his research has focused on visual surveillance and general video analysis. He and his students have developed foundational methods for detection and tracking of people and vehicles in video, representation and recognition of human movements and activities, and mixed AI/signal processing models for event modeling and recognition. He is an Associate Editor of the International Journal of Computer Vision and an area editor for Computer Models for Image Processing: Image Understanding