VASC Seminar: Mikel Rodriguez
Data-Driven Crowd Analysis in Videos
In this work we present a new crowd analysis algorithm powered by behavior priors that are learned on a large database of crowd videos gathered from the Internet. The algorithm works by first learning a set of crowd behavior priors off-line. During testing, crowd patches are matched to the database and behavior priors are transferred. We adhere to the insight that despite the fact that the entire space of possible crowd behaviors is infinite, the space of distinguishable crowd motion patterns may not be all that large. For many individuals in a crowd, we are able to find analogous crowd patches in our database which contain similar patterns of behavior that can effectively act as priors to constrain the difficult task of tracking an individual in a crowd. Our algorithm is data-driven and, unlike some crowd characterization methods, does not require us to have seen the test video beforehand. It performs like state-ofthe-art methods for tracking people having common crowd behaviors and outperforms the methods when the tracked individual behaves in an unusual way.
This is joint work with Josef Sivic, Ivan Laptev and Jean-Yves Audibert.
Host: Gupta, Abhinav
Mikel Rodriguez is a researcher at MITRE. He was a post-doctoral fellow at the INRIA Willow team at the Département d'Informatique of Ecole Normale Supérieure in Paris, France. Mikel completed his PhD in Computer Science at UCF in 2010. His research focuses mainly on video interpretation which includes tracking, visual motion analysis, activity recognition, and crowd behavior analysis.