Posture recognition method of duty personnel based on human posture key points and convolutional neural network

To guarantee the safety and efficiency of industrial production and prevent accidents or losses caused by personnel negligence or negligence, this work proposes a personnel on-duty status recognition method. The method combines a human pose estimation algorithm and a target detection algorithm, which can automatically discriminate six states of personnel on duty. First, the original image is processed using a high-resolution network (HRNet) to generate human pose keypoint maps. Then SE-VGG16 is constructed by combining the squeeze-excitation network and VGG16 for feature extraction of human pose keypoint maps. Finally, the design of the lightweight convolutional neural network for primary classification and you only look once version 5 is used for reclassification for behaviors with similar action features. The experimental results show that the method has an average recognition accuracy of 98.27% with good robustness and generalization ability for six kinds of personnel on-duty status in multiple environments. (c) 2024 SPIE and IS&T