Open Set Video Anomaly Detection (OpenVAD) aims to identify abnormal events from video data where both known anomalies and novel ones exist in testing. Unsupervised models learned solely from normal videos are applicable to any testing anomalies but suffer from a high false positive rate. In contrast, weakly supervised methods are effective in detecting known anomalies but could fail in an open world. We develop a novel weakly supervised method for the OpenVAD problem by integrating evidential deep learning (EDL) and normalizing flows (NFs) into a multiple instance learning (MIL) framework. Specifically, we propose to use graph neural networks and triplet loss to learn discriminative features for training the EDL classifier, where the EDL is capable of identifying the unknown anomalies by quantifying the uncertainty. Moreover, we develop an uncertainty-aware selection strategy to obtain clean anomaly instances and a NFs module to generate the pseudo anomalies. Our method is superior to existing approaches by inheriting the advantages of both the unsupervised NFs and the weakly-supervised MIL framework. Experimental results on multiple real-world video datasets show the effectiveness of our method.
翻译:开放视频异常检测( OpenVAD) 旨在从视频数据中找出已知异常现象和测试中新型异常现象存在的异常事件。 仅从普通视频中学习的未经监督的模式适用于任何测试异常现象,但具有很高的假正率。 相反, 监督不力的方法在发现已知异常现象方面是有效的,但在开放世界中可能失败。 我们开发了一种新颖的、监督不力的开放视频异常现象方法, 将证据深度学习( EDL) 和正常流动( NFs) 纳入多个实例学习框架。 具体而言, 我们提议使用图形神经网络和三重损失来学习用于培训 EDL 分类器的歧视性特征, 在那里, EDL 能够通过量化不确定性来识别未知异常现象。 此外, 我们开发了一种不确定性选择策略, 以获得清洁异常事件和 NFS 模块来生成假异常现象。 我们的方法优于现有方法, 方法是继承未被监视的NFF和弱超超的MIL 框架的优势。 在多个真实世界视频数据集上的实验结果显示了我们的方法的有效性 。