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Automated 2D detection of construction equipment and workers from site video streams using histograms of oriented gradients and colors
Abstract This paper presents a computer vision based algorithm for automated 2D detection of construction workers and equipment from site video streams. The state-of-the-art research proposes semi-automated detection methods for tracking of construction workers and equipment. Considering the number of active equipment and workers on jobsites and their frequency of appearance in a camera's field of view, application of semi-automated techniques can be time-consuming. To address this limitation, a new algorithm based on Histograms of Oriented Gradients and Colors (HOG+C) is proposed. Our proposed detector uses a single sliding window at multiple scales to identify the potential candidates for the location of equipment and workers in 2D. Each detection window is first divided into small spatial regions and then the gradient orientations and hue–saturation colors are locally histogrammed and concatenated to form the HOG+C descriptors. Tiling the sliding detection window with a dense and overlapping grid of formed descriptors and using a binary Support Vector Machine (SVM) classifier for each resource enables automated 2D detection of workers and equipment. A new comprehensive benchmark dataset containing over 8000 annotated video frames including equipment and workers from different construction projects is introduced. This dataset contains a large range of pose, scale, background, illumination, and occlusion variation. Our preliminary results on detection of standing workers, excavators and dump trucks with an average accuracy of 98.83%, 82.10%, and 84.88% respectively indicate the applicability of the proposed method for automated activity analysis of workers and equipment from single video cameras. Unlike other state-of-the-art algorithms in automated resource tracking, this method particularly detects idle resources and does not need manual or semi-automated initialization of the resource locations in 2D video frames. The experimental results and the perceived benefits of the proposed method are discussed in detail.
Graphical abstract Display Omitted Highlights ► We present a computer vision based method for automated 2D resource tracking. ► A comprehensive dataset of excavators, trucks, and standing workers is presented. ► Accuracies for excavators, trucks, and standing workers are 82.10, 84.88, and 98.83%.
Automated 2D detection of construction equipment and workers from site video streams using histograms of oriented gradients and colors
Abstract This paper presents a computer vision based algorithm for automated 2D detection of construction workers and equipment from site video streams. The state-of-the-art research proposes semi-automated detection methods for tracking of construction workers and equipment. Considering the number of active equipment and workers on jobsites and their frequency of appearance in a camera's field of view, application of semi-automated techniques can be time-consuming. To address this limitation, a new algorithm based on Histograms of Oriented Gradients and Colors (HOG+C) is proposed. Our proposed detector uses a single sliding window at multiple scales to identify the potential candidates for the location of equipment and workers in 2D. Each detection window is first divided into small spatial regions and then the gradient orientations and hue–saturation colors are locally histogrammed and concatenated to form the HOG+C descriptors. Tiling the sliding detection window with a dense and overlapping grid of formed descriptors and using a binary Support Vector Machine (SVM) classifier for each resource enables automated 2D detection of workers and equipment. A new comprehensive benchmark dataset containing over 8000 annotated video frames including equipment and workers from different construction projects is introduced. This dataset contains a large range of pose, scale, background, illumination, and occlusion variation. Our preliminary results on detection of standing workers, excavators and dump trucks with an average accuracy of 98.83%, 82.10%, and 84.88% respectively indicate the applicability of the proposed method for automated activity analysis of workers and equipment from single video cameras. Unlike other state-of-the-art algorithms in automated resource tracking, this method particularly detects idle resources and does not need manual or semi-automated initialization of the resource locations in 2D video frames. The experimental results and the perceived benefits of the proposed method are discussed in detail.
Graphical abstract Display Omitted Highlights ► We present a computer vision based method for automated 2D resource tracking. ► A comprehensive dataset of excavators, trucks, and standing workers is presented. ► Accuracies for excavators, trucks, and standing workers are 82.10, 84.88, and 98.83%.
Automated 2D detection of construction equipment and workers from site video streams using histograms of oriented gradients and colors
Memarzadeh, Milad (author) / Golparvar-Fard, Mani (author) / Niebles, Juan Carlos (author)
Automation in Construction ; 32 ; 24-37
2012-12-04
14 pages
Article (Journal)
Electronic Resource
English
| British Library Online Contents | 2013
| British Library Conference Proceedings | 2012