  Aiming at the problem that the current video anomaly detection cannot fully
use the temporal information and ignore the diversity of normal behavior, an
anomaly detection method is proposed to integrate the spatiotemporal
information of pedestrians. Based on the convolutional autoencoder, the input
frame is compressed and restored through the encoder and decoder. Anomaly
detection is realized according to the difference between the output frame and
the true value. In order to strengthen the characteristic information
connection between continuous video frames, the residual temporal shift module
and the residual channel attention module are introduced to improve the
modeling ability of the network on temporal information and channel
information, respectively. Due to the excessive generalization of convolutional
neural networks, in the memory enhancement modules, the hopping connections of
each codec layer are added to limit autoencoders' ability to represent abnormal
frames too vigorously and improve the anomaly detection accuracy of the
network. In addition, the objective function is modified by a feature
discretization loss, which effectively distinguishes different normal behavior
patterns. The experimental results on the CUHK Avenue and ShanghaiTech datasets
show that the proposed method is superior to the current mainstream video
anomaly detection methods while meeting the real-time requirements.
