软件导刊2024，Vol.23Issue(1)：57-62,6.DOI:10.11907/rjdk.222514
工厂场景中的异常行为检测
Abnormal Behavior Detection in Factory Scenarios
摘要
Abstract
A framework for abnormal behavior detection is proposed to address safety production issues in current industrial scenarios,mainly targeting two special situations:workers sleeping and falling.The idea of combining human key point recognition with machine learning classi-fiers is adopted.Firstly,key point recognition is performed on workers in video images,body coordinate point information is extracted,and then the classifier is trained for classification.Multiple machine learning methods and an integrated learning model are used to detect abnormal situations.On the fall dataset,the accuracy,accuracy,and recall of the ensemble learning algorithm reached 92.86%,87.58%,and 98.96%,respectively;In terms of sleep detection,the accuracy,accuracy,and recall of the algorithm reached 98.51%,95.81%,and 94.97%,respectively.Experiments have shown that this framework can effectively detect abnormal situations,help standardize production be-havior,and has practical application value.关键词
行为识别/动作检测/异常行为/跌倒检测/机器学习Key words
behavior recognition/action recognition/abnormal behavior/fall detection/machine learning分类
信息技术与安全科学引用本文复制引用
赵廉,周雷,郭育恒,陈骅桂..工厂场景中的异常行为检测[J].软件导刊,2024,23(1):57-62,6.基金项目
国家自然科学基金项目(61906121) （61906121）
