Learning to detect real-world anomalous events through video-level labels is a challenging task due to the rare occurrence of anomalies as well as noise in the labels. In this work, we propose a weakly supervised anomaly detection method which has manifold contributions including1) a random batch based training procedure to reduce inter-batch correlation, 2) a normalcy suppression mechanism to minimize anomaly scores of the normal regions of a video by taking into account the overall information available in one training batch, and 3) a clustering distance based loss to contribute towards mitigating the label noise and to produce better anomaly representations by encouraging our model to generate distinct normal and anomalous clusters. The proposed method obtains83.03% and 89.67% frame-level AUC performance on the UCF Crime and ShanghaiTech datasets respectively, demonstrating its superiority over the existing state-of-the-art algorithms.

翻译：由于标签中很少出现异常现象和噪音,因此学习通过视频级标签探测真实世界异常事件是一项艰巨的任务。在这项工作中,我们提出一种监督不力的异常现象检测方法,该方法有多种贡献,包括1)一个随机分批培训程序,以减少批量之间的相关性,2)一个正常抑制机制,以尽量减少正常视频区域异常分数,同时考虑到一个培训批中可获得的总体信息,3)基于距离的集群损失,以帮助减少标签噪音,并通过鼓励我们的模型产生独特的正常和异常的群集,产生更好的异常现象表现。拟议方法分别获得了83.03%和89.67%的ACU在UCF犯罪上和上海科技数据组上层框架级的表演,表明其优于现有最新算法。