Real-time vision-based multiple object tracking of a production process: industrial digital twin case study

Robert Ward*, Payam Soulatiantork, Shaun Finneran, Ruby Hughes, Ashutosh Tiwari

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

19 Citations (Scopus)
19 Downloads (Pure)


The adoption of Industry 4.0 technologies within the manufacturing and process industries is widely accepted to have benefits for production cycles, increase system flexibility and give production managers more options on the production line through reconfigurable systems. A key enabler in Industry 4.0 technology is the rise in Cyber-Physical Systems (CPS) and Digital Twins (DTs). Both technologies connect the physical to the cyber world in order to generate smart manufacturing capabilities. State of the art research accurately describes the frameworks, challenges and advantages surrounding these technologies but fails to deliver on testbeds and case studies that can be used for development and validation. This research demonstrates a novel proof of concept Industry 4.0 production system which lays the foundations for future research in DT technologies, process optimisation and manufacturing data analytics. Using a connected system of commercial off-the-shelf cameras to retrofit a standard programmable logic controlled production process, a digital simulation is updated in real time to create the DT. The system can identify and accurately track the product through the production cycle whilst updating the DT in real-time. The implemented system is a lightweight, low cost, customable and scalable design solution which provides a testbed for practical Industry 4.0 research both for academic and industrial research purposes.

Original languageEnglish
Pages (from-to)1861-1872
Number of pages12
JournalProceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture
Issue number11
Early online date12 Mar 2021
Publication statusPublished - 30 Sept 2021


  • cyber-physical production systems
  • digital twin
  • industry 4.0
  • multiple object tracking
  • real time vision tracking
  • smart manufacturing


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