Frame-by-frame crowd motion classification from affine motion models

Abstract

Recognizing dynamic behaviors of dense crowds in videos is of great interest in many surveillance applications. In contrast to most existing methods which are based on trajectories or tracklets, our approach for crowd motion analysis provides a crowd motion classification on a frame-by-frame and pixel-wise basis. Indeed, we only compute affine motion models from pairs of two consecutive video images. The classification itself relies on simple rules on the coefficients of the computed affine motion models, and therefore does not imply any prior learning stage. The overall method proceeds in four steps: (i) detection of moving points, (ii) computation of a set of motion model candidates over a collection of windows, (iii) selection of the best motion model at each point owing to a maximum likelihood criterion, (iv) determination of the crowd motion class at each pixel with a hierarchical classification tree regularized by majority votes. The algorithm is almost parameter-free, and is efficient in terms of memory and computation load. Experiments on computer-generated sequences and real video sequences demonstrate that our method is accurate, and can successfully handle complex situations.