Weak anomaly-reinforced autoencoder for unsupervised anomaly detection
Xinqiang Chen, Lumei Su, Guansen Deng, Huang MingYong, Jia-Jun Wu, Yanqing Peng · 2021
At present, most unsupervised abnormal behavior detection method only relies on powerful behavior detection classifiers, does not make full use of prior knowledge. This method often has the problem of a huge amount of calculation and affecting the detection speed. In view of the above problems, this paper proposes a weak anomalyreinforced autoencoder for unsupervised anomaly detection method, using U-Net to reconstruct video frames and generative adversarial network to learn the correlation between image entropy and abnormal behavior. Comprehensive experiments on the avenue data set and UCSD data sets verify the effectiveness of our method to detect abnormal events.