Vision-based detection and visualization of dynamic workspaces

Xiaochun Luo, Heng Li, Hao Wang, Zezhou Wu, Fei Dai, Dongping Cao

Research output: Journal article publicationJournal articleAcademic researchpeer-review

30 Citations (Scopus)

Abstract

Aligning workers and groups with workspaces in advance to enable peak performance and ensure safety is the essence of workspace planning. There are plans, while there have been few methods for examining these plans. We argue that capturing and visualizing actual dynamic workspaces is fundamental to plan examination. This paper describes an initial effort on integrating the latest computer vision methods to implement automatic detection and visualization of dynamic workspaces of workers on foot. To this end, object detection, multiple object tracking, and action recognition are adopted to collect two types of action data: action classes and action locations. A density-based spatial clustering algorithm is used to reason dynamic workspaces based on the action data. We evaluated each method of the integrated system and found that they have achieved the comparable performance in our envisaged settings with the original methods in their general settings. We also presented two demonstrations of the system. The research results represent an initial step towards developing a new management capability by capturing dynamic workspaces.

Original languageEnglish
Pages (from-to)1-13
Number of pages13
JournalAutomation in Construction
Volume104
DOIs
Publication statusPublished - Aug 2019

Keywords

  • Action recognition
  • Density-based spatial clustering
  • Dynamic workspaces
  • Multiple object tracking

ASJC Scopus subject areas

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

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