An Intelligent Vision-Based Method of Worker Identification for Industrial Internet of Things (IoT). (27th January 2022) - Wang, Shuai; Li, Xiaoyu; Chen, Wei; Fan, Weiqiang; Tian, Zijian
Abstract : With the rapid development of Internet of things (IoT) and computer vision (CV), the application of combining the IoT platform and CV technology to monitor the worker safety has attracted more and more attention in the field of industrial information. Worker identification is a prerequisite for safety management in industrial production, and safety helmet can not only protect worker's head from accidental injuries but also help to identify the work types of workers through different colors. Therefore, this study proposes an intelligent method for worker identification based on moving personnel detection and helmet color characteristics. First, the motion objects that contain personnel and nonpersonnel are detected by the Gaussian mixture model (GMM) and extracted to generate the region of interest (RoI) images. Then, the multiple-scale histogram of oriented gradient (MHOG) features of the RoI images are extracted, and the personnel images are identified by the support vector machine (SVM). Third, the workers' head images are obtained by the OpenPose model and personnel mask, and the GoogLeNet-based transfer learning network is established to extract the head images features and realize worker identification. This method is tested on our dataset, and the average accuracy of worker identification for multiple helmet color combinations reaches 99.43%, which is robust to workers' angle, scale, and occlusion.