A Review of Computer Vision-Based Monitoring Approaches for Construction Workers’ Work-Related Behaviors

Abstract

Construction workers' behaviors directly affects labor productivity and their own safety, thereby influencing project quality. Recognizing and monitoring the construction-related behaviors is therefore crucial for high-quality management and orderly construction site operation. Recent strides in computer vision technology suggest its potential to replace traditional manual supervision approaches. This paper explores research on monitoring construction workers' behaviors using computer vision. Through bibliometrics and content-based analysis, the authors present the latest research in this area from three perspectives: "Detection, Localization, and Tracking for Construction Workers," "Recognition of Workers' Construction Activities," and "Occupational Health and Safety Behavior Monitoring." In terms of the literature's volume, there has been a notable increase in this field. Notably, the focus on safety-related literature is predominant, underscoring the concern for occupational health. Vision algorithms have witnessed an increase in the utilization of object detection. The ongoing and future research trajectory is anticipated to involve multi-algorithm integration and an emphasis on enhancing robustness. Then the authors summarize the review from engineering impact and technical suitability, and analyze the limitations of current research from the perspectives of technical approaches and application scenarios. Finally, it discusses future research directions in this field together with generative AI models. Furthermore, the authors hope this paper can serves as a valuable reference for both scholars and engineers.

Construction Activity Recognition Method Based on Object Detection, Attention Orientation Estimation, and Person Re-Identification

“…Efficiently recognizing construction workers' activities can help project managers control construction progress and allocate on-site labor resources more effectively. This is an important method for analyzing labor productivity [6].…”

“…Most of the traditional monitoring methods are based on manual methods, i.e., there are professionals at the construction site to monitor the construction activities, which has the problems of insufficient coverage, unreasonable personnel scheduling, long time consumption, and strong subjectivity of the monitoring results [4]. With the rapid development of intelligent construction technology [5], a number of advanced hardware devices have been applied to construction activity detection [6]. For example, acceleration sensors, which are widely used in structural health monitoring, have become more portable due to their lightweight characteristics, and related technologies have been applied to worker activity recognition [7][8][9][10].…”

“…Therefore, a more appropriate solution is to use contactless monitoring methods with high accuracy, mainly based on video and image signals, using computer vision techniques that have long been applied in medical diagnostics [11,12], food detection [13,14], vehicle identification [15,16], and structural health monitoring [17][18][19][20][21][22]. The rapid development of deep learning and artificial intelligence technologies offers the possibility of applying computer vision techniques to construction worker activity detection [6].…”