GaugeTracker: AI - Powered Cost-Effective Analog Gauge Monitoring System

Beitong Tian, Mingyuan Wu, Ruixiao Zhang, Haozhen Zheng, Bo Chen, Yaohui Wang, Shiv Trivedi, Shanbo Zhang, Robert Bruce Kaufman, Leah Espenhahn, Gianni Pezzarossi, Mauro Sardela, John Dallesasse, Klara Nahrstedt

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Automating analog gauge readings is essential for providing stakeholders with timely alerts about abnormalities in physical properties measured by gauges, such as pressure, and for offering detailed historical data to improve understanding of the work environment. However, existing systems face challenges in balancing accuracy, continuity, reading latency, network band-width usage, and cost. In this study, we introduce GaugeTracker, an end-to-end system to address these challenges. Our proposed method, based on template matching for gauge reading, precisely determines the current angle of the gauge pointer, significantly outperforming state-of-the-art baselines with an average error of 1.81 degrees. By leveraging the versatility of large vision-language models, we develop a pipeline for automatically generating accurate and realistic gauge templates for each specific gauge at various readings on the server. Deployed on the world's most affordable IoT camera, which is mounted in front of a gauge using our customized camera holder, our prototype system can read the gauge 7 times per second by processing entirely on the device. This delivers continuous and accurate gauge readings across diverse environmental conditions. Furthermore, with a cost of merely $10 per gauge, our system offers a highly cost-effective solution for real-time analog gauge monitoring.

Original languageEnglish (US)
Title of host publicationProceedings - 2024 IEEE 7th International Conference on Multimedia Information Processing and Retrieval, MIPR 2024
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages477-483
Number of pages7
ISBN (Electronic)9798350351422
DOIs
StatePublished - 2024
Externally publishedYes
Event7th IEEE International Conference on Multimedia Information Processing and Retrieval, MIPR 2024 - San Jose, United States
Duration: Aug 7 2024Aug 9 2024

Conference

Conference7th IEEE International Conference on Multimedia Information Processing and Retrieval, MIPR 2024
Country/TerritoryUnited States
CitySan Jose
Period8/7/248/9/24

Keywords

  • Analog Gauge Transcription
  • Computer Vision
  • Internet of Things
  • Large Vision-language Model
  • Real-Time Data Processing
  • Synthetic Data
  • Template Matching

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications
  • Computer Vision and Pattern Recognition
  • Information Systems
  • Media Technology

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