Pedestrian Detection: The State of the Art
Pedestrian detection is a key problem in computer vision, and truly accurate pedestrian detection would have immediate and far reaching impact on areas such as robotics, surveillance, assistive technology for the visually impaired, image indexing, advanced human machine interfaces and automotive safety, among others. In the first part of the talk I will discuss the state of the art in monocular pedestrian detection, including our large-scale benchmarking effort, highlighting current successes and challenges for the research community. In the second part of the talk I will describe our own pedestrian detection approach which achieves top performance in numerous settings across multiple datasets. In particular, I will focus on a recent insight that has allowed us to perform multiscale detection at near real time on standard hardware. Our 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 other domains. Finally, I will conclude by discussing open challenges in pedestrian detection and ideas on how to address them.
Piotr Dollár was born in Krakow, Poland in 1980. 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, especially pedestrian detection and behavior recognition.
- Piotr Dollár