Occlusion and clutter are two scene states that make it difficult to detect anomalies in surveillance video. Furthermore, anomaly events are rare and, as a consequence, class imbalance and lack of labeled anomaly data are also key features of this task. Therefore, weakly supervised methods are heavily researched for this application. In this paper, we tackle these typical problems of anomaly detection in surveillance video by combining Multiple Instance Learning (MIL) to deal with the lack of labels and Multiple Camera Views (MC) to reduce occlusion and clutter effects. In the resulting MC-MIL algorithm we apply a multiple camera combined loss function to train a regression network with Sultani's MIL ranking function. To evaluate the MC-MIL algorithm first proposed here, the multiple camera PETS-2009 benchmark dataset was re-labeled for the anomaly detection task from multiple camera views. The result shows a significant performance improvement in F1 score compared to the single-camera configuration.

翻译：遮挡和杂波是监控视频中难以检测异常的两个场景状态。此外，异常事件罕见，因此类别不平衡和标注异常数据缺乏也是该任务的关键特征。为此，针对该应用广泛研究了弱监督方法。本文通过结合多实例学习(MIL)处理标签缺乏问题以及多摄像头视角(MC)减少遮挡和杂波效应，解决了监控视频异常检测中的这些典型问题。最终提出的MC-MIL算法应用了多摄像头组合损失函数，并结合Sultani的MIL排序函数训练回归网络。为评估首次提出的MC-MIL算法，针对多摄像头PETS-2009基准数据集重新标注了面向多摄像头视角的异常检测任务。结果表明，与单摄像头配置相比，F1分数获得了显著性能提升。