论文标题
在监视视频中以虚假警报率的渐近范围的监视视频中的在线异常检测
Online Anomaly Detection in Surveillance Videos with Asymptotic Bounds on False Alarm Rate
论文作者
论文摘要
监视视频中的异常检测吸引了越来越多的关注。尽管最近的方法具有竞争性能，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神经网络架构。此外，在线决策是该领域中重要但大多被忽略的因素。声称在线的许多现有方法取决于批处理或脱机处理。在这些研究差距的推动下，我们在监视视频中提出了一种在线异常检测方法，其渐近警报率的渐近界限又提供了一个明确的程序，可以选择满足所需错误警报率的适当决策阈值。我们提出的算法由一个多目标深度学习模块以及一个统计异常检测模块组成，并且在几个公开可用的数据集中证明了它的有效性，在该数据集中，我们表现出更优于最先进的算法。所有代码均可在https://github.com/kevaldoshi17/prediction-base-video-anomaly-detection-获得。
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-.
