文章基本信息

标题：Image Processing and Multi-Instance Learning-Based Recognition of Abnormal Behavior in Integrated Pipe Corridors
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作者：Peng Xiao ; Zhenji Zhang ; Laisong Kang 等
期刊名称：Mobile Information Systems
印刷版ISSN：1574-017X
出版年度：2022
卷号：2022
DOI：10.1155/2022/4423016
语种：English
出版社：Hindawi Publishing Corporation
摘要：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.