Crowdsourcing video-based workface assessment for construction activity analysis

Kaijian Liu, Mani Golparvar Fard

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Today, the availability of multiple cameras on every jobsite is reshaping the way construction activities are being monitored. Research has focused on addressing the limitations of manual workface assessment from these videos via computer vision algorithms. Despite the rapid explosion of these algorithms, the ability to automatically recognize worker and equipment activities from videos is still limited. By crowd-sourcing the task of workface assessment from jobsite videos, this paper aims to overcome the limitations of the current practice and provides a large empirical dataset that can serve as the basis for developing video-based activity recognition methods. As such, an intuitive web-based platform for massive marketplaces such as Amazon Mechanical Turk (AMT) is introduced that engages the intelligence of non-expert crowd for interpretations of selected group of frames from these videos and then it automates remaining workface assessment tasks based on the initial interpretations. To validate, several experiments are conducted on videos from concrete placement operations. The results show that engaging AMT non-experts together with computer vision algorithms can provide assessment results with an accuracy of 85%. This minimizes the time needed for workface assessment, and allows the practitioners to focus their time on the more important task of root-cause analysis for performance improvements. This platform also provides significantly large datasets with ground truth for algorithmic development purposes.

Original languageEnglish (US)
Title of host publication32nd International Symposium on Automation and Robotics in Construction and Mining
Subtitle of host publicationConnected to the Future, Proceedings
PublisherInternational Association for Automation and Robotics in Construction I.A.A.R.C)
ISBN (Electronic)9789517585972
StatePublished - Jan 1 2015
Event32nd International Symposium on Automation and Robotics in Construction and Mining: Connected to the Future, ISARC 2015 - Oulu, Finland
Duration: Jun 15 2015Jun 18 2015

Publication series

Name32nd International Symposium on Automation and Robotics in Construction and Mining: Connected to the Future, Proceedings

Other

Other32nd International Symposium on Automation and Robotics in Construction and Mining: Connected to the Future, ISARC 2015
CountryFinland
CityOulu
Period6/15/156/18/15

Keywords

  • Computer vision
  • Construction productivity
  • Crowdsourcing
  • Workface assessment

ASJC Scopus subject areas

  • Building and Construction
  • Artificial Intelligence
  • Civil and Structural Engineering
  • Hardware and Architecture

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  • Cite this

    Liu, K., & Golparvar Fard, M. (2015). Crowdsourcing video-based workface assessment for construction activity analysis. In 32nd International Symposium on Automation and Robotics in Construction and Mining: Connected to the Future, Proceedings (32nd International Symposium on Automation and Robotics in Construction and Mining: Connected to the Future, Proceedings). International Association for Automation and Robotics in Construction I.A.A.R.C).