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.
国家科技期刊平台
NSTL
万方数据
维普
