计算机工程2025，Vol.51Issue(9)：59-70,12.DOI:10.19678/j.issn.1000-3428.0069349
基于知识图谱的异常个体提前识别模型研究
Knowledge Graph-based Advance Recognition Model for Abnormal Individuals
摘要
Abstract
The identification of abnormal individuals in videos is an important research topic in the field of computer vision.Existing algorithms primarily focus on detecting the outbreak phase of abnormal behaviors but overlook their developmental stage.Moreover,they suffer from issues such as unclear definitions of abnormalities,poor interpretability,and weak generalizability across application scenarios.To address these problems,this study proposes a knowledge graph-based model for the early identification of abnormal individuals.The model performs pedestrian detection and tracking,pedestrian visual attention target detection,and pedestrian behavior recognition from videos to capture pedestrian attribute features related to abnormal behaviors.Moreover,the study establishes a knowledge graph network targeting abnormal individuals and proposes four node modeling algorithms,including those for age attributes and social distance.Nodes are modeled based on pedestrian attributes to better analyze the characteristics of abnormal individuals during the developmental stage of abnormal behaviors.Additionally,the study proposes three abnormal individual reasoning algorithms based on node state transitions for child abduction,theft,robbery,and fighting.These algorithms perform state reasoning on knowledge graph nodes to derive the probability of an individual engaging in abnormal behavior in the future,thereby enabling the early identification of abnormal individuals.The reasoning algorithms adopted enhance the interpretability of the model.An early abnormal individual detection dataset is created and annotated,defining four types of abnormal behaviors:theft,fighting,robbery,and child abduction.The samples in the dataset are sourced from various shooting scenarios.The effectiveness of the model is evaluated on this dataset,and the experimental results show that the model achieves a mean Average Precision(mAP)of 22.83％,outperforming other mainstream behavior recognition models.Specifically,it demonstrates an 18.96 percentage point improvement over the SlowFast model,indicating that the proposed model can effectively identify abnormal individuals before the outbreak of abnormal behaviors and is generalizable across application scenarios.关键词
异常行为/提前识别/行为识别/异常检测/行人属性/知识图谱Key words
abnormal behavior/advance recognition/behavior recognition/anomaly detection/pedestrian attribute/knowledge graph分类
信息技术与安全科学引用本文复制引用
徐式芃,王雷,盛捷..基于知识图谱的异常个体提前识别模型研究[J].计算机工程,2025,51(9):59-70,12.基金项目
高技术创新特区项目(20-163-14-LZ-001-004-01). （20-163-14-LZ-001-004-01）
