Visible to the public Video anomaly detection method based on future frame prediction and attention mechanism

TitleVideo anomaly detection method based on future frame prediction and attention mechanism
Publication TypeConference Paper
Year of Publication2021
AuthorsWang, Chenxu, Yao, Yanxin, Yao, Han
Conference Name2021 IEEE 11th Annual Computing and Communication Workshop and Conference (CCWC)
Date Publishedjan
Keywordsanomaly detection, attention mechanism, Conferences, deep video, Gallium nitride, generative adversarial network, generative adversarial networks, Generators, Markov processes, Metrics, Prediction algorithms, pubcrawl, resilience, Resiliency, Scalability, video anomaly detection, video frame prediction
AbstractWith the development of deep learning technology, a large number of new technologies for video anomaly detection have emerged. This paper proposes a video anomaly detection algorithm based on the future frame prediction using Generative Adversarial Network (GAN) and attention mechanism. For the generation model, a U-Net model, is modified and added with an attention module. For the discrimination model, a Markov GAN discrimination model with self-attention mechanism is proposed, which can affect the generator and improve the generation quality of the future video frame. Experiments show that the new video anomaly detection algorithm improves the detection performance, and the attention module plays an important role in the overall detection performance. It is found that the more the attention modules are appliedthe deeper the application level is, the better the detection effect is, which also verifies the rationality of the model structure used in this project.
Citation Keywang_video_2021