Person anomaly detection-based videos surveillance system in urban integrated pipe gallery

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.