Section: New Results

Crowd motion classification

Participants : Antoine Basset, Charles Kervrann, Patrick Bouthemy.

Important research efforts have been devoted to crowd analysis for several years [58] , [65] . We are interested in this topic for two main reasons. First, views of crowded scenes are not that different of light microscopy intracellular images. Second, the addressed problem, i.e. motion understanding, is common, and we are investigating similar data-driven methodological approaches. This a way to cross-fertilize two domains.

We address the problem of classifying coherent crowd motions in videos recorded by a fixed camera. 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 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 three steps: we first compute a set of motion model candidates on a collection of windows of different sizes in the image, then we select the motion model at each point owing to a ML criterion, finally we determine the crowd motion class map with a hierarchical classification tree regularized by majority votes. The algorithm is almost parameter-free, and is extremely efficient in terms of memory and computation load. Experiments on computer-generated sequences [28] and real video sequences demonstrate that our method is accurate, and can successfully handle complex situations (see Fig. 12 ).

References:[14] , [23]

Figure 12. Two frames of the Marathon bend sequence. People run from upper left to upper right, describing a U. The movement is quite constant in the whole sequence and so is the classification: in the left branch, people go South (magenta), then turn counterclockwise (red) until the end of the bend. Some Eastward translation (yellow) is sometimes found here because of the large radius of curvature. Finally the North translation is recovered (blue). The points in the upper right corner of the image are classified as translations to the West (purple), but the translation direction is closer to North than to West (North-North-West): it is also due to the lateral presence of pedestrians walking to the left.