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《工程(英文)》 >> 2019年 第5卷 第5期 doi: 10.1016/j.eng.2019.02.008

AED-Net——异常事件检测网络

a School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, China
b Department of Electronic Engineering, Dalian Maritime University, Dalian 116026, China
c School of Instrumentation Science and Opto-electronic Engineering & International Research Institute for Multidisciplinary Science, Beihang University, Beijing 100191, China
d Institute Charles Delaunay-LM2S-UMR STMR 6281 CNRS, University of Technology of Troyes, Troyes 10010, France

收稿日期: 2018-05-22 修回日期: 2019-02-01 录用日期: 2019-02-25 发布日期: 2019-05-25

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摘要

长期以来,在拥挤场景中检测异常事件都是一项具有挑战性的任务。为解决这一问题,本文提出了一种名叫异常事件检测网络(AED-Net)的自监督框架,它由主成分分析网络(PCANet)和核主成分分析(kPCA)组成。该框架以不同场景的监控视频序列为原始数据,通过训练PCANet以提取人群情况的高级语义。kPCA可作为一种单类分类器,被用于识别场景中的异常事件。与目前流行的一些深度学习方法相比,该框架完全是自监督的,因为它只使用正常情况下的视频序列。通过对明尼苏达大学公共监测人类活动数据集(UMN数据集)和加州大学圣地亚哥分校监测异常数据集(UCSD数据集)进行全局和局部异常事件进行检测发现,与其他最先进的方法相比,该方法具有更好的等误差率(EER)和曲线下面积(AUC)。此外,通过增加局部响应归一化(LRN)层,我们对原有的AED-Net进行了改进。结果表明,该改进版在提高框架的泛化能力方面表现出更好的性能。

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