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.

随着信息技术的发展和监控网络的普及，如何快速，自动地检测监控视频中的异常行为，对于公安和智慧城市越来越重要。深度学习的出现极大地促进了异常检测的发展，并且在该主题上已经进行了很多出色的工作。但是，现有的异常检测方法通常会遇到诸如运动模式利用不足以及不同数据集不稳定等问题。为了提高监视视频中异常检测的性能，我们提出了一种基于双向预测的框架，该框架分别通过前向和后向预测子网预测相同的目标帧。然后，基于真实目标帧及其双向预测帧构造损失函数。此外，我们还提出了一种基于滑动窗口方案的异常得分估计方法，该方法着重于预测误差图的前景。与最新技术的比较表明，在不同的视频监控数据集上，所提出的模型优于大多数竞争模型。