Event Location: NSH 1305
Bio: Irfan Essa is a Professor in the School of Interactive Computing (iC) of the College of Computing (CoC), and Adjunct Professor in the School of Electrical and Computer Engineering, Georgia Institute of Technology (GA Tech), in Atlanta, Georgia, USA. He is also the Director of Off-Campus Initiatives for the College of Computing. He works in the areas of Computer Vision, Computer Graphics, Computational Perception, Robotics and Computer Animation, Machine Learning, and Social Computing, with potential impact on Video Analysis and Production (e.g., Computational Photography & Video, Image-based Modeling and Rendering, etc.) Human Computer Interaction, and Artificial Intelligence research. His specific research interests are in Video Analysis & Synthesis, and Activity & Behavior Recognition. He also works in the new area of Computational Journalism. Specifically, he is interested in the analysis, interpretation, authoring, and synthesis (of video), with the goals of building aware environments & supporting healthy living, recognizing & modeling human behaviors, empowering humans to effectively interact with each other, with media & with technologies, and developing dynamic & generative representations of time-varying streams. He has published over 150 scholarly articles in leading journals and conference venues on these topics. He has been awarded numerous awards, including the NSF CAREER award and is currently an IEEE Fellow. For more information, see his website http://prof.irfanessa.com

Abstract: In this talk, I will discuss a variety of approaches my group is working on for video analysis and enhancement. In particular, I will describe our approach for a video stabilizer, currently implemented and running on YouTube, and its extensions.

This method generates stabilized videos by employing L1-optimal camera paths to remove undesirable motions [1]. We compute camera paths that are optimally partitioned into constant, linear and parabolic segments mimicking the camera motions employed by professional cinematographers. We propose a linear programming framework to minimize the first, second, and third derivatives of the resulting camera path. Our method allows for video stabilization beyond the conventional filtering that only suppresses high frequency jitter. An additional challenge in videos shot from mobile phones are rolling shutter distortions. Modern CMOS cameras capture the frame one scan-line at a time, which results in non-rigid image distortions such as shear and wobble. I will demonstrate a solution based on a novel mixture model of homographies parametrized by scan-line blocks to correct these rolling shutter distortions [2]. Our method does not rely on a-priori knowledge of the readout time nor requires prior camera calibration. A thorough evaluation based on a user study and direct comparisons to other approaches, demonstrates general preference for our algorithm.

I will conclude the talk by showcasing a live demo of the stabilizer. This work is in collaboration with Matthias Grundmann and Vivek Kwatra at Google, and appears in following two papers.

Time permitting, I will discuss some other projects we are working on, including video segmentation and retargetting.

[1] Matthias Grundmann, Vivek Kwatra, Irfan Essa, CVPR 2011, www.cc.gatech.edu/cpl/projects/videostabilization
[2] Matthias Grundmann, Vivek Kwatra, Daniel Castro Irfan Essa, ICCP 2012, Best paper, www.cc.gatech.edu/cpl/projects/rollingshutter