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.

中文翻译：

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用于视觉异常检测的认知记忆增强网络

随着自动化视觉分析的快速发展，视觉分析系统已成为计算机视觉和自动化分析领域的热门研究课题。视觉分析系统可以帮助人类检测异常事件（例如，打架、独自在草地上行走等）。一般来说，现有的视觉异常检测方法通常基于自编码器架构，即重建当前帧或预测未来帧。然后，采用重构误差作为评估指标来识别输入是否异常。现有方法的缺陷是异常样本也可以很好地重建。在这篇论文中，受人类记忆能力的启发，我们为视觉异常检测问题提出了一种新的基于深度神经网络 (DNN) 的模型，称为认知记忆增强网络 (CMAN)。提出的 CMAN 模型假设视觉分析系统模仿人类记住正常样本，然后从收集的视频中区分异常事件。具体来说，在提出的 CMAN 模型中，我们引入了一个能够模拟人类记忆容量的记忆模块和一个可以学习数据分布的密度估计网络。重建错误和新颖性分数用于区分视频中的异常事件。此外，我们开发了一个两步方案来训练所提出的模型，以便所提出的内存模块和密度估计网络可以合作以提高性能。在各种流行基准上评估的综合实验表明，与最先进的方法相比，所提出的 CMAN 模型在视觉异常检测方面的优越性和有效性。我们的 CMAN 方法的实现代码可以在 https://github.com/CMAN-code/CMAN_pytorch 访问。