Data-driven Crowd Analysis in Videos Mikel Rodriguez1,4 Josef Sivic2,4 Ivan Laptev2,4 Jean-Yves Audibert3,4

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 ad-here to the insight that despite the fact that the entire space of possible crowd behaviors is infinite, the space of distin-guishable crowd motion patterns may not be all that large. For many individuals in a crowd, we are able to find anal-ogous crowd patches in our database which contain sim-ilar 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-of-the-art methods for tracking people having common crowd behaviors and outperforms the methods when the tracked individual behaves in an unusual way. 1