Vision-based monitoring of site safety compliance based on worker re-identification and personal protective equipment classification

Construction sites are highly hazardous due to the dynamic interaction between workers and moving equipment, with high fatality rates caused by collision and falling from height, etc. Hence, identifying unsafe behaviors among workers is crucial for enhancing site safety, such as tracking their on-site movement and personal protective equipment (PPE). Vision-based video processing has been actively used to automatically recognize workers and their behaviors on construction sites. However, existing studies mainly monitor workers within a single camera capturing only a small sub-region. As workers typically move around fairly large sites, continuously tracking their movement across multiple cameras would enable more comprehensive behavioral analyses. Hence, this paper proposes a framework for monitoring safety compliance among workers, by combining worker re-identification (ReID) and PPE classification. Deep learning-based approaches are developed to address the challenges for these two tasks respectively. For ReID, a new loss function named similarity loss is designed to encourage deep learning models to learn more discriminative human features, realizing a more robust tracking of individual workers. For classifying PPE statuses, a weighted-class strategy is proposed to mitigate model bias when given imbalanced samples among classes, for improved performance despite limited training samples. By combining the ReID and PPE classification results, a workflow is developed to log any incident of workers not wearing the necessary PPEs. With an actual construction site dataset, the proposed methods improve worker ReID and PPE classification by 4% and 13% accuracies respectively, which will facilitate site video analytics and inspection of site safety compliance among workers.