A Cognitive Memory-Augmented Network for Visual Anomaly Detection

doi:10.1109/JAS.2021.1004045

CASIA OpenIR > 学术期刊 > IEEE/CAA Journal of Automatica Sinica

	A Cognitive Memory-Augmented Network for Visual Anomaly Detection
	Tian Wang; Xing Xu; Fumin Shen; Yang Yang
发表期刊	IEEE/CAA Journal of Automatica Sinica
ISSN	2329-9266
	2021
卷号	8 期号:7 页码:1296-1307
摘要	With the rapid development of automated visual analysis, visual analysis systems have become a popular research topic in the field of computer vision and automated analysis. Visual analysis systems can assist humans to detect anomalous events (e.g., fighting, walking alone on the grass, etc). In general, the existing methods for visual anomaly detection are usually based on an autoencoder architecture, i.e., reconstructing the current frame or predicting the future frame. Then, the reconstruction error is adopted as the evaluation metric to identify whether an input is abnormal or not. The flaws of the existing methods are that abnormal samples can also be reconstructed well. In this paper, inspired by the human memory ability, we propose a novel deep neural network (DNN) based model termed cognitive memory-augmented network (CMAN) for the visual anomaly detection problem. The proposed CMAN model assumes that the visual analysis system imitates humans to remember normal samples and then distinguishes abnormal events from the collected videos. Specifically, in the proposed CMAN model, we introduce a memory module that is able to simulate the memory capacity of humans and a density estimation network that can learn the data distribution. The reconstruction errors and the novelty scores are used to distinguish abnormal events from videos. In addition, we develop a two-step scheme to train the proposed model so that the proposed memory module and the density estimation network can cooperate to improve performance. Comprehensive experiments evaluated on various popular benchmarks show the superiority and effectiveness of the proposed CMAN model for visual anomaly detection comparing with the state-of-the-arts methods. The implementation code of our CMAN method can be accessed at https://github.com/CMAN-code/CMAN_pytorch.
关键词	Cognitive computing density estimation memory visual analysis systems visual anomaly detection
DOI	10.1109/JAS.2021.1004045
引用统计	被引频次：15[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/44583
专题	学术期刊_IEEE/CAA Journal of Automatica Sinica
推荐引用方式 GB/T 7714	Tian Wang,Xing Xu,Fumin Shen,et al. A Cognitive Memory-Augmented Network for Visual Anomaly Detection[J]. IEEE/CAA Journal of Automatica Sinica,2021,8(7):1296-1307.
APA	Tian Wang,Xing Xu,Fumin Shen,&Yang Yang.(2021).A Cognitive Memory-Augmented Network for Visual Anomaly Detection.IEEE/CAA Journal of Automatica Sinica,8(7),1296-1307.
MLA	Tian Wang,et al."A Cognitive Memory-Augmented Network for Visual Anomaly Detection".IEEE/CAA Journal of Automatica Sinica 8.7(2021):1296-1307.

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