Recovery of motion patterns and dominant paths in videos of crowded scenes

Assessing crowd behaviors from videos is a difficult task while of interest in many applications. We have defined a novel approach which identifies from two successive frames only, crowd behaviors expressed by simple image motion patterns. It relies on the estimation of a collection of sub-affine motion models in the image, a local motion classification based on a penalized likelihood criterion, and a regularization stage involving inhibition and reinforcement factors. We have also developed an original and simple method for recovering the dominant paths followed by people in the observed scene. It involves the introduction of local paths determined from the space-time average of the parametric motion subfields selected in each image block. Experiments on synthetic and real scenes have demonstrated the performance of our method.