Automatic key frame extraction in continuous videos from construction monitoring by using color, texture, and gradient features

Ling Chen, Yuhong Wang

Research output: Journal article publicationJournal articleAcademic researchpeer-review

27 Citations (Scopus)

Abstract

On-site video recording systems are increasingly used for monitoring construction activities. The recorded videos contain rich and useful jobsite information that can be used for a variety of purposes. The large amount of video data generated by continuous monitoring, however, creates tremendous challenges on data storage and retrieval. Due to the relatively slow pace of construction activities, a significant portion of the recorded data is redundant. Therefore, archiving raw construction videos into a concise and structured set of key frames would facilitate data storage, retrieval and analysis. Three key issues in automatic key frame extraction from construction videos are studied, including the selection of proper video features, scene segmentation, and key frame extraction. New image features and methods are developed to address the three issues. A validation experiment indicates that the developed features and methods can effectively and efficiently extract representative key frames from the complex and dynamic construction videos. The developed techniques can be used to develop a construction video summary system that serves the purpose of effectively archiving construction jobsite videos.
Original languageEnglish
Pages (from-to)355-368
Number of pages14
JournalAutomation in Construction
Volume81
DOIs
Publication statusPublished - 1 Sept 2017

Keywords

  • Construction video archiving
  • Image feature selection
  • Key frame extraction
  • Scene transition detection

ASJC Scopus subject areas

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

Fingerprint

Dive into the research topics of 'Automatic key frame extraction in continuous videos from construction monitoring by using color, texture, and gradient features'. Together they form a unique fingerprint.

Cite this