论文标题
在监视视频中持续学习异常检测
Continual Learning for 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。诸如视频监视之类的高维应用程序的一个具有挑战性的方面是持续学习。尽管当前最新的深度学习方法在现有的公共数据集上表现良好，但由于计算和存储问题，它们无法在持续的学习框架中工作。此外，在线决策是该领域中重要但主要被忽略的因素。在这些研究差距的激励下，我们提出了一种使用转移学习和持续学习的在线异常检测方法，用于监视视频，这反过来又大大降低了培训的复杂性，并提供了一种机制，可以从最近的数据中不断学习而不会遭受灾难性遗忘。我们提出的算法利用了基于神经网络的模型的特征提取能力转移学习，以及统计检测方法的持续学习能力。
Anomaly detection in surveillance videos has been recently gaining attention. A challenging aspect of high-dimensional applications such as video surveillance is continual learning. While current state-of-the-art deep learning approaches perform well on existing public datasets, they fail to work in a continual learning framework due to computational and storage issues. 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 continual learning, which in turn significantly reduces the training complexity and provides a mechanism for continually learning from recent data without suffering from catastrophic forgetting. Our proposed algorithm leverages the feature extraction power of neural network-based models for transfer learning, and the continual learning capability of statistical detection methods.
