ABSTRACT The integrated pipe gallery, also known as urban lifeline, is a significant content of the smart city. While the video surveillance system is a crucial part of the integrated pipe gallery, which provides a basis for the construction of smart city. Due to the large amount of video data, manual monitoring is a time-consuming and laborious task. To address the above problems, we propose a neural network-based method that incorporates the concept of area under curve (AUC) with the multiple-instance learning (MIL) approach. We formulate the multiple-instance AUC (MIAUC) model that predicts high anomaly scores for anomalous segments. Furthermore, sparsity and temporal smoothness constraints are utilized in the loss function to better detect anomaly. To verify the effectiveness of our proposed method, a new database is established based on the video surveillance system, which consists of 110 real-world surveillance videos with a total length of 24 h. The experimental results on the real-world database show that our method achieves better performance as compared to the baselines methods. Moreover, we design a MIAUC-based video surveillance system and the practical effect reveals the prospect of utilizing the MIL method for person anomaly detection in the integrated pipe gallery.

中文翻译：

---

基于人员异常检测的城市综合管廊视频监控系统

摘要 综合管廊又称城市生命线，是智慧城市的重要内容。而视频监控系统是综合管廊的重要组成部分，为智慧城市的建设提供了基础。由于视频数据量大，人工监控是一项费时费力的工作。为了解决上述问题，我们提出了一种基于神经网络的方法，该方法将曲线下面积 (AUC) 的概念与多实例学习 (MIL) 方法相结合。我们制定了多实例 AUC (MIAUC) 模型，该模型可预测异常段的高异常分数。此外，在损失函数中利用稀疏性和时间平滑性约束来更好地检测异常。为了验证我们提出的方法的有效性，基于视频监控系统建立了一个新的数据库，该数据库由110个真实世界的监控视频组成，总时长为24小时。在真实世界数据库上的实验结果表明，与基线方法相比，我们的方法取得了更好的性能。此外，我们设计了一个基于 MIAUC 的视频监控系统，实际效果揭示了利用 MIL 方法进行综合管廊人员异常检测的前景。