Deep social force network for anomaly event detection

Abstract

Anomaly event detection is vital in surveillance video analysis. However, how to learn the discriminative motion in the crowd scene is still not tackled. Here, a deep social force network by exploiting both social force extracting and deep motion coding is proposed. Given a grid of particles with velocity provided by the optical flow, the interaction force in the crowd scene is investigated and a social force module is embedded in a deep network. A deep motion convolution was further designed with a 3D (DMC-3D) module. The DMC-3D not only eliminates the noise motion in the crowd scene with a spatial encoder–decoder but also learns the 3D feature with a spatio-temporal encoder. The deep social force coding is modelled with multiple features, in which each feature can describe specific anomaly motion. The experiments on UCF-Crime and ShanghaiTech datasets demonstrate that our method can predict the temporal localization of anomaly events and outperform the state-of-the-art methods.