Automated 2D detection of construction equipment and workers from site video streams using histograms of oriented gradients and colors

Milad Memarzadeh, Mani Golparvar-Fard, Juan Carlos Niebles

Research output: Contribution to journalArticlepeer-review

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 automated2Ddetection ofworkers 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 inautomated 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.

Original languageEnglish (US)
Pages (from-to)24-37
Number of pages14
JournalAutomation in Construction
Volume32
DOIs
StatePublished - 2013

Keywords

  • HSV colors
  • Histogram of oriented gradients
  • Performance monitoring
  • Resource tracking
  • Support vector machine

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Civil and Structural Engineering
  • Building and Construction

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