Weighted Interaction Force Estimation for Abnormality Detection in Crowd Scenes

Abstract. In this paper, we propose a weighted interaction force esti-mation in the social force model(SFM)-based framework, in which the properties of surrounding individuals in terms of motion consistence, dis-tance apart, and angle-of-view along moving directions are fully utilized in order to more precisely discriminate normal or abnormal behaviors of crowd. To avoid the challenges in object tracking in crowded videos, we rst perform particle advection to capture the continuity of crowd ow and use these moving particles as individuals for the interaction force estimation. For a more reasonable interaction force estimation, we joint-ly consider the properties of surrounding individuals, assuming that the individuals with consistent motion (as a particle group) and the ones out of the angle-of-view have no in uence on each other, besides the farther apart ones have weaker in uence. In particular, particle groups are clustered by spectral clustering algorithm, in which a novel and high discriminative gait feature in frequency domain, combined with spatial and motion feature, is used. The estimated interaction forces are mapped to image span to form force ow, from which bag-of-word features are extracted. Sparse Topical Coding (STC) model is used to nd abnor-mal events. Experiments conducted on three datasets demonstrate the promising performance of our work against other related ones.