Anomaly detection in surveillance video based on bidirectional prediction

Abstract

With the development of information technology and the popularization of monitoring network, how to quickly and automatically detect abnormal behaviors in surveillance video is becoming more and more important for public security and smart city. The emergence of deep learning has greatly promoted the development of anomaly detection and much remarkable work has been presented on this topic. However, the existing approaches for anomaly detection generally encounter problems such as insufficient utilization of motion patterns and instability on different datasets. To improve the performance of anomaly detection in surveillance video, we propose a framework based on bidirectional prediction, which predicts the same target frame by the forward and the backward prediction subnetworks, respectively. Then the loss function is constructed based on the real target frame and its bidirectional prediction frame. Furthermore, we also propose an anomaly score estimation method based on the sliding window scheme which focuses on the foregrounds of the prediction error map. The comparison with the state-of-the-art shows that the proposed model outperforms most competing models on different video surveillance datasets.