  Video anomaly detection is commonly used in many applications such as
security surveillance and is very challenging.A majority of recent video
anomaly detection approaches utilize deep reconstruction models, but their
performance is often suboptimal because of insufficient reconstruction error
differences between normal and abnormal video frames in practice. Meanwhile,
frame prediction-based anomaly detection methods have shown promising
performance. In this paper, we propose a novel and robust unsupervised video
anomaly detection method by frame prediction with proper design which is more
in line with the characteristics of surveillance videos. The proposed method is
equipped with a multi-path ConvGRU-based frame prediction network that can
better handle semantically informative objects and areas of different scales
and capture spatial-temporal dependencies in normal videos. A noise tolerance
loss is introduced during training to mitigate the interference caused by
background noise. Extensive experiments have been conducted on the CUHK Avenue,
ShanghaiTech Campus, and UCSD Pedestrian datasets, and the results show that
our proposed method outperforms existing state-of-the-art approaches.
Remarkably, our proposed method obtains the frame-level AUROC score of 88.3% on
the CUHK Avenue dataset.

Computer Vision and Pattern Recognition;Machine Learning