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-.

监视视频中的异常检测正引起越来越多的关注。尽管最近的方法具有竞争优势，但它们缺乏理论上的性能分析，特别是由于决策中使用了复杂的深度神经网络架构。此外，在线决策是该领域中重要但也是最常被忽略的因素。实际上，许多声称是联机的方法都依赖于批处理或脱机处理。基于这些研究空白，我们提出了一种在虚警率上具有渐近边界的监视视频中的在线异常检测方法，这反过来为选择满足期望的虚警率的正确决策阈值提供了清晰的过程。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，其有效性在多个公开可用的数据集上得到了证明，而我们在这些数据集上的表现优于最先进的算法。所有代码都可以在https://github.com/kevaldoshi17/Prediction-based-Video-Anomaly-Detection-获得。