Title page for ETD etd-12112012-103535


Type of Document Master's Thesis
Author Memarzadeh, Milad
URN etd-12112012-103535
Title Automated 2D Detection and Localization of Construction Resources in Support of Automated Performance Assessment of Construction Operations
Degree Master of Science
Department Civil Engineering
Advisory Committee
Advisor Name Title
Golparvar-Fard, Mani Committee Chair
de la Garza, Jesus M. Committee Member
Marr, Linsey C. Committee Member
Niebles, Juan Carlos Committee Member
Keywords
  • Support Vector Machine
  • Histogram of Oriented Gradients
  • Deformable Part-based Models
  • HSV Colors
  • Resource Detection and Localization
  • Performance Monitoring
Date of Defense 2012-12-10
Availability unrestricted
Abstract
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.
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