Online anomaly detection in surveillance videos with asymptotic bound on false alarm rate
Online anomaly detection in surveillance videos with asymptotic bound on false alarm rate
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DOI：
10.1016/j.patcog.2021.107865
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发表时间：
2021-02-16
影响因子：
8
通讯作者：
Yilmaz, Yasin
中科院分区：
文献类型：
--
作者：
Doshi, Keval;Yilmaz, Yasin
监控视频中的异常检测越来越受到人们的关注。尽管最近的方法具有竞争力的性能，但它们缺乏理论性能分析，特别是由于决策中使用的复杂的深度神经网络架构。此外，在线决策是这一领域中一个重要但大多被忽视的因素。许多声称在线的现有方法实际上依赖于批处理或离线处理。受这些研究差距的启发，我们提出了一种监控视频中的在线异常检测方法，该方法具有虚警率的渐进界限，这反过来又为选择满足所需虚警率的适当决策阈值提供了明确的程序。我们提出的算法由一个多目标深度学习模块沿着一个统计异常检测模块组成，其有效性在几个公开的数据集上得到了证明，在这些数据集上，我们的性能优于最先进的算法。所有代码都可以在https://github.com/kevaldoshi17/Prediction-based-Video-Anomaly-Detection-上找到。(c)2021爱思唯尔有限公司版权所有。
Anomaly detection in surveillance videos is attracting an increasing amount of attention. Despite the competitive performance of recent methods, they lack theoretical performance analysis, particularly due to the complex deep neural network architectures used in decision making. Additionally, online decision making is an important but mostly neglected factor in this domain. Much of the existing methods that claim to be online, depend on batch or offline processing in practice. Motivated by these research gaps, we propose an online anomaly detection method in surveillance videos with asymptotic bounds on the false alarm rate, which in turn provides a clear procedure for selecting a proper decision threshold that satisfies the desired false alarm rate. Our proposed algorithm consists of a multi-objective deep learning module along with a statistical anomaly detection module, and its effectiveness is demonstrated on several publicly available data sets where we outperform the state-of-the-art algorithms. All codes are available at https://github.com/kevaldoshi17/Prediction-based-Video-Anomaly-Detection-.(c) 2021 Elsevier Ltd. All rights reserved.