摘要
随着视频监控数据的快速增长,对大规模视频数据的自动异常检测的需求越来越大,基于深度自编码器重构误差检测方法已经被广泛探讨。但是,有时自编码器“泛化”得很好,能够很好地重建异常并导致漏检。为了解决这个问题,提出了采用记忆力模块来增强自动编码器,称为记忆力增强自编码(Memory-augmented autoencoder, Memory AE)方法。给定输入,Memory AE首先从编码器获取编码,然后将其用作查询以检索最相关的记忆项来进行重建。在训练阶段,记忆内容被更新以表示正常数据的原型元素。在测试阶段,将学习到的记忆元素固定下来,从正常数据的几个选定的记忆记录中获得重建,因此重建将趋向于接近正常样本。因此,将加强对异常的重构误差以进行异常检测。对两个公共视频异常检测数据集,即Avenue数据集和ShanghaiTech数据集的研究证明了所提出方法的有效性。
With the rapid growth of video surveillance data, there is an increasing demand for video anomaly detection, and reconstruction error detection methods based on deep autoencoders have been widely discussed.However, the autoencoder generalizes well, can reconstruct the anomaly well and lead to missed detection. In order to solve this problem, this paper proposes to adopt a memory module to enhance the autoencoder, which is called the Memory-augmented autoencoder(Memory AE) method. Given the input, Memory AE first obtains the encoding from the encoder, and then uses it as a query to retrieve the most relevant memory items for reconstruction. In the training phase, the memory content is updated and encouraged to represent prototype elements of normal data. In the test phase, the learned memory elements are fixed, and reconstruction is obtained from several selected memory records of normal data, thus the reconstruction will tend to be close to normal samples. Therefore, the reconstruction of abnormal errors will be strengthened for abnormal detection.Experiments on two public video anomaly detection datasets, namely Avenue dataset and ShanghaiTech dataset,proves the effectiveness of the proposed method.
作者
孙敬波
季节
Sun Jingbo;Ji Jie(School of Mathematics and Computer Application Technology Jining University,Qufu 273155,China)
出处
《红外与激光工程》
EI
CSCD
北大核心
2022年第6期358-364,共7页
Infrared and Laser Engineering
关键词
异常事件检测
视频监控
自编码网络
记忆力增强
深度学习
anomalous event detection
video surveillance
auto-encoding network
memory-augmented model
deep learning
