An object recognition, tracking, and contextual reasoning-based video interpretation method for rapid productivity analysis of construction operations

Jie Gong, Carlos H. Caldas

Research output: Contribution to journalArticlepeer-review

103 Scopus citations

Abstract

Measuring the process of construction operations for productivity improvement remains a difficult task for most construction companies due to the manual effort required in most activity measurement methods. This paper proposed and described the elements, processes, and algorithms that comprise a computational and intelligent construction video interpretation method. A number of vision-based construction object recognition and tracking methods were evaluated to provide guidance for algorithm selection. A prototype system was developed to integrate the proposed video analysis processes and selected computer vision algorithms. Videos of construction operations were analyzed to validate the proposed method. Comparing to the traditional manual construction video analysis method, the proposed method provided a semi-automated video interpretation method. The new method enabled the interpretation of these videos into productivity information, such as working processes, cycle times, and delays, with an accuracy that was comparable to manual analysis, without the limitations of on-site human observation.

Original languageEnglish (US)
Pages (from-to)1211-1226
Number of pages16
JournalAutomation in Construction
Volume20
Issue number8
DOIs
StatePublished - Dec 2011
Externally publishedYes

All Science Journal Classification (ASJC) codes

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

Keywords

  • Automated data collection and analysis
  • Computer vision
  • Construction productivity
  • Process measurement
  • Video interpretation

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