论文标题
监视视频中的任何射击顺序异常检测
Any-Shot Sequential Anomaly Detection in Surveillance Videos
论文作者
论文摘要
监视视频中的异常检测最近引起了人们的关注。即使最先进的方法在公开可获得的数据集上的表现具有竞争力，但他们仍需要大量的培训数据。同样，一旦获得新数据，他们就缺乏连续更新经过训练的模型的具体方法。此外，在线决策是该领域中重要但主要被忽略的因素。在这些研究差距的推动下，我们提出了一种在线异常检测方法，用于使用转移学习和任何射击学习的监视视频，从而大大降低了训练的复杂性，并提供了一种只能使用几个标记的名义示例来检测异常的机制。我们提出的算法利用基于神经网络的模型的特征提取能力转移学习和统计检测方法的任何射击学习能力。
Anomaly detection in surveillance videos has been recently gaining attention. Even though the performance of state-of-the-art methods on publicly available data sets has been competitive, they demand a massive amount of training data. Also, they lack a concrete approach for continuously updating the trained model once new data is available. Furthermore, online decision making is an important but mostly neglected factor in this domain. Motivated by these research gaps, we propose an online anomaly detection method for surveillance videos using transfer learning and any-shot learning, which in turn significantly reduces the training complexity and provides a mechanism that can detect anomalies using only a few labeled nominal examples. Our proposed algorithm leverages the feature extraction power of neural network-based models for transfer learning and the any-shot learning capability of statistical detection methods.
