Vision-Based Real-Time Posture Tracking for Multiple Construction Workers

Abstract

Tracking the postures of construction workers can provide precious information for safety management, occupational illness prevention, and productivity investigation. However, the posture data of construction workers is rarely utilized due to a lack of appropriate methods to track it. This research proposes a real-time multiworker posture tracking (MWPT) method to accurately track the postures of multiple workers onsite from video streams. It consists of three elements: image enhancement to adapt varying light conditions, posture detection for obtaining workers’ postures, and matching for tracking and retracking postures. In the field experiment, MWPT performed satisfactorily with an average of two ID switches (IDS), an average frame per second (FPS) of 11.0, and an average precision (AP@50) of 86.33. The results prove the capability of MWPT for tracking multiworker postures in real construction environments with high robustness and effectiveness. This research not only contributes an innovative tracking algorithm but also lays a stepping stone toward further worker posture-related research. This research introduces a posture tracking method for multiple construction workers. The motivation behind this research stems from the absence of an effective tracking method tailored to the demanding conditions of construction sites, such as variable lighting, extensive occlusions, and the challenge of distinguishing workers with similar appearances. The proposed solution leverages posture similarity for tracking workers and incorporates a retracking mechanism to address visual occlusions. The contrast limited adaptive histogram equalization method is employed to recover high-contrast visual features from dimly lit images, effectively addressing the issue of insufficient lighting. This method has been validated as both effective and efficient through customized test videos from actual construction sites. It shows promise for use in unsafe behavior detection, occupational disease prevention, and other posture-related research or applications as an effective tool.