Enhancing action recognition of construction workers using data-driven scene parsing

Yang, Jun

doi:10.3846/jcem.2018.6133

Cited by 13 publications

(5 citation statements)

References 52 publications

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“…For instance, Liu et al [125] utilized a silhouettebased approach, while Yang et al [108] employed an SVM approach for classifying image features. However, with technological advancements, Yang et al [126] enhanced the method by incorporating data-driven scene parsing while retaining the dense trajectories.…”

Section: B Recognition Of Workers' Construction Activitiesmentioning

confidence: 99%

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

Li,

Miao,

Zou

et al. 2024

IEEE Access

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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.

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Section: B Recognition Of Workers' Construction Activitiesmentioning

confidence: 99%

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

Li,

Miao,

Zou

et al. 2024

IEEE Access

View full text Add to dashboard Cite

show abstract

“…In early construction activity recognition research [46][47][48], traditional image processing methods are more commonly used. However, with the development and improvement of CNN, the training cost of the object detection algorithm has been further reduced, resulting in improved detection speed and accuracy.…”

Section: Computer Vision Application In Workers' Construction Activit...mentioning

confidence: 99%

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

Li,

Zhao,

Kong

et al. 2024

Buildings

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Recognition and classification for construction activities help to monitor and manage construction workers. Deep learning and computer vision technologies have addressed many limitations of traditional manual methods in complex construction environments. However, distinguishing different workers and establishing a clear recognition logic remain challenging. To address these issues, we propose a novel construction activity recognition method that integrates multiple deep learning algorithms. To complete this research, we created three datasets: 727 images for construction entities, 2546 for posture and orientation estimation, and 5455 for worker re-identification. First, a YOLO v5-based model is trained for worker posture and orientation detection. A person re-identification algorithm is then introduced to distinguish workers by tracking their coordinates, body and head orientations, and postures over time, then estimating their attention direction. Additionally, a YOLO v5-based object detection model is developed to identify ten common construction entity objects. The worker’s activity is determined by combining their attentional orientation, positional information, and interaction with detected construction entities. Ten video clips are selected for testing, and a total of 745 instances of workers are detected, achieving an accuracy rate of 88.5%. With further refinement, this method shows promise for a broader application in construction activity recognition, enhancing site management efficiency.

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“…Different action recognition techniques were established for recording construction worker actions [41]. Yang et al introduced a scene-parsing system using semantic information to enhance the action recognition of workers [45]. Although CV-based worker monitoring systems have been deployed across various construction-site scenarios, employing various techniques for multiple tasks, the effectiveness of these methods diminishes with environmental changes and variations in training data specific to certain conditions, leading to missed and false detection [46].…”

Section: Computer Vision Techniques For Construction Safety Monitoringmentioning

confidence: 99%

Vision-Based Construction Safety Monitoring Utilizing Temporal Analysis to Reduce False Alarms

Zaidi,

Yang,

Abbas

et al. 2024

Buildings

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Construction safety requires real-time monitoring due to its hazardous nature. Existing vision-based monitoring systems classify each frame to identify safe or unsafe scenes, often triggering false alarms due to object misdetection or false detection, which reduces the overall monitoring system’s performance. To overcome this problem, this research introduces a safety monitoring system that leverages a novel temporal-analysis-based algorithm to reduce false alarms. The proposed system comprises three main modules: object detection, rule compliance, and temporal analysis. The system employs a coordination correlation technique to verify personal protective equipment (PPE), even with partially visible workers, overcoming a common monitoring challenge on job sites. The temporal-analysis module is the key component that evaluates multiple frames within a time window, triggering alarms when the hazard threshold is exceeded, thus reducing false alarms. The experimental results demonstrate 95% accuracy and an F1-score in scene classification, with a notable 2.03% average decrease in false alarms during real-time monitoring across five test videos. This study advances knowledge in safety monitoring by introducing and validating a temporal-analysis-based algorithm. This approach not only improves the reliability of safety-rule-compliance checks but also addresses challenges of misdetection and false alarms, thereby enhancing safety management protocols in hazardous environments.

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Enhancing action recognition of construction workers using data-driven scene parsing

Cited by 13 publications

References 52 publications

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

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

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

Vision-Based Construction Safety Monitoring Utilizing Temporal Analysis to Reduce False Alarms

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