Construction performance monitoring via still images, time-lapse photos, and video streams: Now, tomorrow, and the future

Jun Yang, Man Woo Park, Patricio A. Vela, Mani Golparvar-Fard

Research output: Contribution to journalArticlepeer-review

Abstract

Timely and accurate monitoring of onsite construction operations can bring an immediate awareness on project specific issues. It provides practitioners with the information they need to easily and quickly make project control decisions. Despite their importance, the current practices are still time-consuming, costly, and prone to errors. To facilitate the process of collecting and analyzing performance data, researchers have focused on devising methods that can semi-automatically or automatically assess ongoing operations both at project level and operation level. A major line of work has particularly focused on developing computer vision techniques that can leverage still images, time-lapse photos and video streams for documenting the work in progress. To this end, this paper extensively reviews these state-of-the-art vision-based construction performance monitoring methods. Based on the level of information perceived and the types of output, these methods are mainly divided into two categories (namely project level: visual monitoring of civil infrastructure or building elements vs. operation level: visual monitoring of construction equipment and workers). The underlying formulations and assumptions used in these methods are discussed in detail. Finally the gaps in knowledge that need to be addressed in future research are identified.

Original languageEnglish (US)
Pages (from-to)211-224
Number of pages14
JournalAdvanced Engineering Informatics
Volume29
Issue number2
DOIs
StatePublished - Apr 1 2015

Keywords

  • Computer vision
  • Construction
  • Machine learning
  • Performance monitoring

ASJC Scopus subject areas

  • Information Systems
  • Artificial Intelligence

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