Crowd motion partitioning in a scattered motion field.

Abstract

In this paper, we propose a crowd motion partitioning approach based on local-translational motion approximation in a scattered motion field. To represent crowd motion in an accurate and parsimonious way, we compute optical flow at the salient locations instead of at all the pixel locations. We then transform the problem of crowd motion partitioning into a problem of scattered motion field segmentation. Based on our assumption that local crowd motion can be approximated by a translational motion field, we develop a local-translation domain segmentation (LTDS) model in which the evolution of domain boundaries is derived from the Gâteaux derivative of an objective functional and further extend LTDS to the case of scattered motion field. The experiment results on a set of synthetic vector fields and a set of videos depicting real-world crowd scenes indicate that the proposed approach is effective in identifying the homogeneous crowd motion components under different scenarios.