Citation

Shan-wu YN, Hong-bing X, Yu W, Mei SUN. J. Graphics 2023; 44(1): e95.

Copyright

(Copyright © 2023, China Association for Science and Technology)

DOI

10.11996/JG.j.2095-302X.2023010095

PMID

unavailable

Abstract

To address the current problem that video anomaly detection cannot make full use of temporal information and ignores the diversity of normal behaviors, an anomaly detection method incorporating pedestrian spatiotemporal information was proposed. Based on the convolutional auto-encoder, the input frames were compressed and reduced by the encoder and decoder in it, and the anomaly detection was realized according to the difference between the output frames and the real value. In order to strengthen the feature information connection between consecutive frames of the video, the residual time shift module and the residual channel attention module were introduced to enhance the network's ability to model temporal and channel information, respectively. Considering the overgeneralization of the convolutional neural networks (CNN), a memory-augmented module was added between the skip connections of each layer of the encoder and decoder to limit the overpowering representation of anomalous frames by the auto-encoder and improve the anomaly detection accuracy of the network. In addition, the objective function was modified by a kind of feature separateness loss to effectively distinguish different normal behavior patterns. Experimental results on the CUHK Avenue and ShanghaiTech datasets show that the proposed method outperforms the current mainstream video anomaly detection methods while meeting the real-time requirements.

Key words: video anomaly detection, unsupervised learning, spatiotemporal two-stream network, auto-encoder

Language: en