Learning Object-Level Spatio-Temporal Representation for Abnormal Event Detection

Abstract

In this paper, we propose an approach for abnormal event detection, using the object-level spatio-temporal representation. Our approach detects an abnormal event in complex scenes which contain objects classified in various categories. We compute the object-level 3D Region-of-interest (3D RoI) and extract object-level 3D volume. Then, the object-level 3D volume is inputted to 3D deep convolutional neural network (3D-DCNN) for detecting the abnormal event. In the experiments, we compare our method with several methods on our experimental dataset.