Synthetic Data for Evaluating the Visual Tracking of Construction Workers

Neuhausen, Marcel; Herbers, Patrick; König, Markus

doi:10.1061/9780784482865.038

Cited by 3 publications

(1 citation statement)

References 15 publications

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“…Zhang and Ge [77] employed the latest Transformer technology to identify dynamic collision risks between construction workers and tower cranes. Additionally, other studies have contributed to collision risk identification [64], [147], [148]and fall risk identification [149]. Table 4 presents some of the typical research literature in this field.…”

Section: Computer Vision Techniques Facilitated In Extracting Postura...mentioning

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: Computer Vision Techniques Facilitated In Extracting Postura...mentioning

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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Robust appearance modeling for object detection and tracking: a survey of deep learning approaches

Mumuni

2022

Prog Artif Intell

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We review current and emerging knowledge-informed and brain-inspired cognitive systems for realizing adversarial defenses, eXplainable Artificial Intelligence (XAI), and zero-shot or few-shot learning. Data-driven deep learning models have achieved remarkable performance and demonstrated capabilities surpassing human experts in many applications. Yet, their inability to exploit domain knowledge leads to serious performance limitations in practical applications. In particular, deep learning systems are exposed to adversarial attacks, which can trick them into making glaringly incorrect decisions. Moreover, complex data-driven models typically lack interpretability or explainability, i.e., their decisions cannot be understood by human subjects. Furthermore, models are usually trained on standard datasets with a closed-world assumption. Hence, they struggle to generalize to unseen cases during inference in practical open-world environments, thus, raising the zero-or few-shot generalization problem. Although many conventional solutions exist, explicit domain knowledge, brain-inspired neural network and cognitive architectures offer powerful new dimensions towards alleviating these problems. Prior knowledge is represented in appropriate forms and incorporated in deep learning frameworks to improve performance. Brain-inspired cognition methods use computational models that mimic the human mind to enhance intelligent behavior in artificial agents and autonomous robots. Ultimately, these models achieve better explainability, higher adversarial robustness and data-efficient learning, and can, in turn, provide insights for cognitive science and neurosciencethat is, to deepen human understanding on how the brain works in general, and how it handles these problems.Index Terms-Domain knowledge, cognitive architecture, brain-inspired neural network, explainable AI, adversarial attack, zero-shot generalization. ✦ INTRODUCTION State of deep learningMachine learning is an artificial intelligence (AI) concept that enables computing systems to learn useful relationships from data and then use this information to identify learned patterns and make predictions on new inputs. Deep learning (DL) is a machine learning method that uses a multi-layered arrangement of computational units to learn relevant patterns from data. Many deep learning constructs have been proposed for various use-cases. Some of the most popular ones are deep convolutional neural networks (DCNN) [1], [2] recurrent neural networks (RNN) [3], generative adversarial networks (GANs) [4], deep reinforcement learning [5] and vision transformers [6], [7]). DCNNs are a class of deep neural networks (DNNs) specially designed to handle image data.Recently, deep learning methods have accomplished remarkable milestones in many critical applications. In medical domains, some state-of-the-art models (e.g., as reported

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Recognition of temporary vertical objects in large point clouds of construction sites

Vega

Braun

Noichl

et al. 2021

Proceedings of the Institution of Civil Engineers - Smart Infra

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Although adherence to project schedule is the most critical performance metric among project owners, still 53 % of typical construction projects exhibit schedule delays. While construction progress monitoring is key to allow effective project management, it is still a largely manual, error prone and inefficient process. To contribute to more efficient construction progress monitoring, this research proposes a method to automatically detect the most common temporary object classes in large-scale laser scanner point clouds of construction sites. Finding the position of these objects in the point cloud can help determine the current state of construction progress and verify compliance with safety regulations. The proposed workflow includes a combination of several techniques: image processing over vertical projections of point clouds, finding patterns in 3D detected contours, and performing checks over vertical cross-sections with deep learning methods. After applying and testing the method on three real-world point clouds and testing with three object categories (cranes, scaffolds, and formwork), the results reveal that our technique achieves rates above 88 % for precision and recall and outstanding computational performance. These metrics demonstrate the method’s capability to support the automatic 3D object detection in point clouds of construction sites.

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Synthetic Data for Evaluating the Visual Tracking of Construction Workers

Cited by 3 publications

References 15 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

Robust appearance modeling for object detection and tracking: a survey of deep learning approaches

Recognition of temporary vertical objects in large point clouds of construction sites

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