Automated Worker Activity Analysis in Indoor Environments for Direct-Work Rate Improvement from Long Sequences of RGB-D Images

Khosrowpour, Ardalan; Fedorov, Igor; Holynski, Aleksander; Juan,; Niebles, Carlos; Golparvar-Fard, Mani

doi:10.1061/9780784413517.075

Cited by 15 publications

(7 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, it can be analyzed and simplified into a repetitive sequence of individual activities in accordance with the process of workface assessment. For example, Khosrowpour et al took interior drywall operation as a case study and divided the operations into seven categories [78]. In line with the principle of activity analysis, and with the assistance of interviews from onsite scaffolders and scaffolding supervisors, we analyze the scaffolding operation and categorize it into the three following sections below:…”

Section: Activity Definitionmentioning

confidence: 99%

Automatic Scaffolding Workface Assessment for Activity Analysis through Machine Learning

et al. 2021

View full text Add to dashboard Cite

Scaffolding serves as one construction trade with high importance. However, scaffolding suffers from low productivity and high cost in Australia. Activity Analysis is a continuous procedure of assessing and improving the amount of time that craft workers spend on one single construction trade, which is a functional method for monitoring onsite operation and analyzing conditions causing delays or productivity decline. Workface assessment is an initial step for activity analysis to manually record the time that workers spend on each activity category. This paper proposes a method of automatic scaffolding workface assessment using a 2D video camera to capture scaffolding activities and the model of key joints and skeleton extraction, as well as machine learning classifiers, were used for activity classification. Additionally, a case study was conducted and showed that the proposed method is a feasible and practical way for automatic scaffolding workface assessment.

show abstract

Section: Activity Definitionmentioning

confidence: 99%

Automatic Scaffolding Workface Assessment for Activity Analysis through Machine Learning

et al. 2021

View full text Add to dashboard Cite

show abstract

“…The former category has mostly focused on articulated resources, such as personnel and machines (Peddi et al, 2009;Weerasinghe and Ruwanpura, 2010). By decoding the articulation poses or target feature elements over time, the activity category can be inferred (Khosrowpour et al, 2014;Yang et al, 2015). Work activities may be broken into effective work, ineffective work, or contributory work for productivity analysis (Peddi et al, 2009).…”

Section: Activity Analysismentioning

confidence: 99%

Fusion of Photogrammetry and Video Analysis for Productivity Assessment of Earthwork Processes

Bügler

Borrmann

Ogunmakin

et al. 2016

Computer aided Civil Eng

View full text Add to dashboard Cite

The high complexity of modern large‐scale construction projects leads their schedules to be sensitive to delays. At underground construction sites, the earthwork processes are vital, as most of the following tasks depend on them. This article presents a method for estimating the productivity of soil removal by combining two technologies based on computer vision: photogrammetry and video analysis. Photogrammetry is applied to create a time series of point clouds throughout excavation, which are used to measure the volume of the excavated soil for daily estimates of productivity. Video analysis is used to generate statistics regarding the construction activities for estimating productivity at finer time scales, when combined with the output from the photogrammetry pipeline. As there may be multiple causes for specific productivity levels, the automated generation of progress and activity statistics from both measurement methods supports interpretation of the productivity estimates. Comparison to annotated ground truth for the tracking and activity monitoring method highlights the reliability of the extracted information. The suitability of the approach is demonstrated by two case studies of real‐world urban excavation projects.

show abstract

“…The depth information is computed based on the distortions of structured lights which project a known pattern of infrared light dots onto the scene (Chen et al, 2013). This type of sensor has been limited to use in lab settings (Escorcia et al, 2012) or indoor construction sites (Khosrowpour et al, 2014).…”

Section: Range Sensorsmentioning

confidence: 99%

Tracking-based 3D human skeleton extraction from stereo video camera toward an on-site safety and ergonomic analysis

Liu

Han

Lee

2016

View full text Add to dashboard Cite

Purpose As a means of data acquisition for the situation awareness, computer vision-based motion capture technologies have increased the potential to observe and assess manual activities for the prevention of accidents and injuries in construction. This study thus aims to present a computationally efficient and robust method of human motion data capture for the on-site motion sensing and analysis. Design/methodology/approach This study investigated a tracking approach to three-dimensional (3D) human skeleton extraction from stereo video streams. Instead of detecting body joints on each image, the proposed method tracks locations of the body joints over all the successive frames by learning from the initialized body posture. The corresponding body joints to the ones tracked are then identified and matched on the image sequences from the other lens and reconstructed in a 3D space through triangulation to build 3D skeleton models. For validation, a lab test is conducted to evaluate the accuracy and working ranges of the proposed method, respectively. Findings Results of the test reveal that the tracking approach produces accurate outcomes at a distance, with nearly real-time computational processing, and can be potentially used for site data collection. Thus, the proposed approach has a potential for various field analyses for construction workers’ safety and ergonomics. Originality/value Recently, motion capture technologies have rapidly been developed and studied in construction. However, existing sensing technologies are not yet readily applicable to construction environments. This study explores two smartphones as stereo cameras as a potentially suitable means of data collection in construction for the less operational constrains (e.g. no on-body sensor required, less sensitivity to sunlight, and flexible ranges of operations).

show abstract

Automated Worker Activity Analysis in Indoor Environments for Direct-Work Rate Improvement from Long Sequences of RGB-D Images

Cited by 15 publications

References 10 publications

Automatic Scaffolding Workface Assessment for Activity Analysis through Machine Learning

Automatic Scaffolding Workface Assessment for Activity Analysis through Machine Learning

Fusion of Photogrammetry and Video Analysis for Productivity Assessment of Earthwork Processes

Tracking-based 3D human skeleton extraction from stereo video camera toward an on-site safety and ergonomic analysis

Contact Info

Product

Resources

About