Adaptive computer vision-based 2D tracking of workers in complex environments

Konstantinou, Eirini; Lasenby, Joan; Brilakis, Ioannis

doi:10.1016/j.autcon.2019.01.018

Cited by 51 publications

(30 citation statements)

References 30 publications

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“…Traditionally, productivity has been investigated with observations (Costin et al, 2012); however, human inspections and observations are tedious and not feasible for conducting continuously on a construction site due to the slow process of data collection and analysis (Akhavian and Behzadan, 2016). In order to automate tracking, researchers have explored the use of computer visionbased techniques (Yang et al, 2016;Luo et al, 2018;Konstantinou et al, 2019).…”

Section: Task Management and Production Control In Constructionmentioning

confidence: 99%

“…Also, existing vision-based tracking methods lack applicability because they usually require human operators to calibrate monitoring when encountering congestion. Construction workers often need to wear specific clothes, such as hi-vis apparel, to create a necessary tracking environment for image recognition (Konstantinou et al, 2019).…”

Section: Task Management and Production Control In Constructionmentioning

confidence: 99%

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Using Real-Time Indoor Resource Positioning to Track the Progress of Tasks in Construction Sites

Zhao

Pikas

Seppänen

et al. 2021

Front. Built Environ.

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Lean construction methods have demonstrated potential to improve construction productivity. For example, the location-based management system and the last planner system have increased the reliability of planning and control in construction production. However, these benefits are often reduced because of inaccurate manual data collection. To alleviate these problems, technologies for automated monitoring of workers have been developed to identify site events in chaotic environments. This paper aims to investigate whether a Bluetooth low-energy-based real-time indoor positioning system can monitor task progress from workers’ presence. Our findings suggest that the proposed system is a feasible solution for monitoring task-level progress when there are explicit dependencies between tasks. This method could automatically detect task start and finish times and estimate the hours required to complete a task. This enables the measurement of waste hidden inside tasks, which allows for interventions for improving flows and eliminating waste.

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Section: Task Management and Production Control In Constructionmentioning

confidence: 99%

Section: Task Management and Production Control In Constructionmentioning

confidence: 99%

Using Real-Time Indoor Resource Positioning to Track the Progress of Tasks in Construction Sites

Zhao

Pikas

Seppänen

et al. 2021

Front. Built Environ.

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show abstract

“…A kapcsolódó szenzorok és eszközök távoli és valós idejű adatgyűjtést tesznek lehetővé. Ide tartoznak a kamerák (Konstantinou et al, 2019), a szenzorok és az intelligens címkék (RFID, BLE) továbbá a robotika és a drónok. Ezek a technológiák kiemelten segítik az olyan munkakörnyezetet, ahol különösen fontos a munkavédelem és a biztonság.…”

Section: Technológiák éS Az éRtéklánc Kapcsolataunclassified

Építőipar 4.0 • Construction 4.0

Nagy¹,

Zs.²

2021

Ma.Tud.

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“…Their method was able to achieve 86.4% accuracy without human-labeled training data. Similar to the concept of online learning, Konstantinou et al (2019) proposed an adaptive model to track construction workers continuously. With the remarkable advances in deep learning algorithms, researchers have investigated deep neural networks (DNNs) for construction object detection and tracking.…”

Section: Construction Object Detection and Trackingmentioning

confidence: 99%

Visual Analytics for Operation-Level Construction Monitoring and Documentation: State-of-the-Art Technologies, Research Challenges, and Future Directions

Kim

2020

Front. Built Environ.

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Operation-level vision-based monitoring and documentation has drawn significant attention from construction practitioners and researchers. To automate the operation-level monitoring of construction and built environments, there have been much effort to develop computer vision technologies. Despite their encouraging findings, it remains a major challenge to exploit technologies in real construction projects, implying that there are knowledge gaps in practice and theory. To fill such knowledge gaps, this study thoroughly reviews 119 papers on operation-level vision-based construction monitoring, published in mainstream construction informatics journals. Existing research papers can be categorized into three sequential technologies: (1) camera placement for operation-level construction monitoring, (2) single-camera-based construction monitoring and documentation, and (3) multi-camera-based onsite information integration and construction monitoring. For each technology, state-of-the-art algorithms, open challenges, and future directions are discussed.

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Adaptive computer vision-based 2D tracking of workers in complex environments

Cited by 51 publications

References 30 publications

Using Real-Time Indoor Resource Positioning to Track the Progress of Tasks in Construction Sites

Using Real-Time Indoor Resource Positioning to Track the Progress of Tasks in Construction Sites

Építőipar 4.0 • Construction 4.0

Visual Analytics for Operation-Level Construction Monitoring and Documentation: State-of-the-Art Technologies, Research Challenges, and Future Directions

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