Object-Centered Visual Recognition
Machine vision approaches for accurate and efficient visual recognition have the potential to play a transformative role in numerous applications, and while seemingly simple visual tasks continue to elude artificial systems, we are making rapid progress. In this talk I will discuss my research on “object-centered” visual recognition, where the goal is to find objects in images and videos and characterize their attributes and activity in detail. Specifically, I will discuss my work on four fundamental aspects of object-centered visual recognition: object detection, pose estimation, object tracking and behavior recognition. First, I will describe our state-of-the-art pedestrian detection approach with a focus on a recent insight that has allowed us to perform accurate multiscale detection in near real time. This approach yields a speedup of 10-100 times over competing methods with only a minor loss in detection accuracy and the underlying theory should be readily applicable to numerous domains. I will also briefly discuss our large-scale benchmarking of pedestrian detection, highlighting current successes and open challenges for the research community. In the second part of the talk I will describe our method for efficient pose estimation, a general tracking by detection system which leverages our research in object detection and pose estimation, and our widely adopted framework for behavior recognition. The approaches I will present are both effective (i.e., accurate and robust) and practical (i.e., computationally efficient and broadly applicable) and these are elements I will highlight throughout the talk.
Piotr Dollár was born in Krakow, Poland. Shortly after moving to the United States in 1985, he received bachelor of arts (A.B.) and master of science (S.M.) degrees in computer science from Harvard in 2002 (concurrently). His Ph.D. work, also in computer science, was performed at the University of California San Diego under the guidance of Serge Belongie and supported by an NSF IGERT fellowship. After receiving his Ph.D. in 2007, he went on to become a postdoctoral fellow at the Computational Vision lab at Caltech under Pietro Perona where he currently resides. His research interests lie in machine learning and pattern recognition and their application to computer vision.
- Piotr Dollar
- California Institute of Technology