Falling Detection Study on Granary Workers Based on Human Pose Estimation
In recent years,there have been frequent safety issues such as the inability of grain warehouse workers to self rescue due to accidental falls into grain piles,and fainting due to poisoning during fumigation operations.In or-der to timely obtain effective information and reduce harm to workers,a bottom-up human pose estimation method is proposed,and a state detection model for granary workers is established.Firstly,we produced a public dataset of grain bin operation videos and falling states from different angles;Then we improved the lightweight OpenPose algorithm and introduced the dataset into the algorithm to generate skeletal images;Finally,we designed a falling detection al-gorithm to obtain a fall detection model for grain bin operators based on pose estimation.The experimental results show that the accuracy of detecting fall state is improved,and the lightweight OpenPose algorithm has lower computa-tional complixity and small memory occupation,and can detect the fall state of grain bin operators in real-time.
Human pose estimationFall detectionLightweight algorithm
NETL
NSTL
万方数据
