Automated 2D Detection and Localization of Construction Resources in Support of Automated Performance Assessment of Construction Operations

• Main Author: Memarzadeh, Milad
• Other Authors: Civil Engineering
• Format: Others
• Language: en_US
• Published: Virginia Tech 2017
• Subjects: Support Vector Machine; Histogram of Oriented Gradients; Deformable Part-based Models; HSV Colors; Resource Detection and Localization; Performance Monitoring
• Online Access: http://hdl.handle.net/10919/76908; http://scholar.lib.vt.edu/theses/available/etd-12112012-103535/

This study presents two computer vision based algorithms 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, two new algorithms based on Histograms of Oriented Gradients and Colors (HOG+C), 1) HOG+C sliding detection window technique, and 2) HOG+C deformable part-based model are proposed and their performance are compared to the state-of-the-art algorithm in computer vision community. Furthermore, a new comprehensive benchmark dataset containing over 8,000 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. The preliminary results with average performance accuracies of 100%, 92.02%, and 89.69% for workers, excavators, and dump trucks respectively, indicate the applicability of the proposed methods for automated activity analysis of workers and equipment from single video cameras. Unlike other state-of-the-art algorithms in automated resource tracking, these methods particularly detects idle resources and does not need manual or semi-automated initialization of the resource locations in 2D video frames. === Master of Science