Image Processing and Multi-Instance Learning-Based Recognition of Abnormal Behavior in Integrated Pipe Corridors

Abstract

The use of big data technology to efficiently access valid corridor monitoring information embedded in unstructured data and to achieve fast and effective processing of video surveillance data is an effective means of monitoring abnormal behavior in integrated corridors. The study first divides the longer surveillance video into multiple parts and then extracts functions for each part based on CenterNet. Inspired by the area under the curve concept, MIAUC was further applied to a loss function model, which encouraged higher scores for anomalous segments compared to normal segments. Also, by formulating anomaly detection as a regression problem, methods based on weakly labeled training data will consider both normal and anomalous behavior for anomaly detection. To alleviate the difficulty of obtaining accurate segment-level labels, Multiple Instance Learning (MIL) is utilized to learn the anomaly model and detect video segment-level anomalies during testing. The results of the research enable effective 24/7 monitoring, storage functions, intrusion detection functions, and emergency linkage functions.