多层记忆增强生成对抗网络二次预测的视频异常检测方法

为了提高视频异常检测的准确率，提出了一种基于多层记忆增强生成对抗网络二次预测的视频异常检测方法。首先利用目标检测提取时空立方体，并将其输入自编码器中得到预测帧；其次将预测帧的表观特征和对应真实帧的光流特征进行融合，形成融合特征；最后利用多层记忆增强生成对抗网络二次预测未来帧，以便学习不同层次特征的正常模式并捕获上下文的语义信息。在UCSD Ped2和CUHK Avenue数据集上进行的实验结果表明：所提出的方法与其他视频异常检测方法相比，可有效提高视频异常检测的性能，使帧级别AUC分别达到99.57%和91.59%。

Video Anomaly Detection Method Based on Secondary Prediction of Multi-layer Memory Enhancement Generative Adversarial Network

In order to improve the accuracy of video anomaly detection, we propose a video anomaly detection method based on secondary prediction of multi-layer memory enhancement generative adversarial networks. Firstly, a spatiotemporal cube is extracted from target detection, and sent into encoder to obtain a prediction frame. Secondly, the apparent feature of the prediction frame and the optical flow feature of corresponding real frames are fused to form fusion features. Finally, a secondary prediction future frame is generated by using multi-layer memory enhancement generative adversarial networks, for learning normal feature patterns of different levels and capturing the semantic information of context. Experimental results on UCSD Ped2 and CUHK Avenue datasets show that the proposed method can effectively improve the performance of video anomaly detection compared with other video anomaly detection methods, and its frame level AUC reaches 99.57% and 91.59%, respectively.