Deep learning and anthropometric plane based workflow monitoring by detecting and tracking workers

N. A. Gard, J. Chen, P. Tang, A. Yilmaz

Research output: Contribution to journalConference articlepeer-review

2 Scopus citations


The worker productivity, a critical variable in project management, significantly affects the progress of a project. The key to measuring productivity is analysis of activities, which provides necessary information by identifying how workers spend their time at certain areas in the site. In this work, we propose a novel joint image-trajectory space for automatic detection and tracking of workers using a single fixed camera. A two-branch convolutional neural network detects workers and their body joints. Instead of tracking the body joints in the image space, we transform detected joints onto virtual parallel planes called “Anthropometric Planes”. The detected joints are, then, tracked using a Kalman Filter on these planes which are created based on anthropometric measures of an average American male. Finally, an uncertainty measure is introduced to reduce the number of identity changes and to handle missing joints. The experiments conducted on an image sequence captured in a nuclear plant shows promising detection and tracking results.

Original languageEnglish (US)
Pages (from-to)149-154
Number of pages6
JournalInternational Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives
Issue number1
StatePublished - Sep 20 2018
Event2018 ISPRS Technical Commission I Midterm Symposium on Innovative Sensing - From Sensors to Methods and Applications - Karlsruhe, Germany
Duration: Oct 10 2018Oct 12 2018


  • Anthropometric Measures
  • Critical Path
  • Detection
  • Tracking

ASJC Scopus subject areas

  • Information Systems
  • Geography, Planning and Development


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