Privacy aware crowd-counting using thermal cameras

Rita Tse, Tianchen Wang, Marcus Im, Giovanni Pau

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

4 Citations (Scopus)


Visual analytics has been in the limelight as a powerful tool to support large scale management of places, people, and activities. Harnessing the power of machine learning is possible to quickly identify critical issues across thousands of cameras. Several stakeholders have voiced concerns about privacy. Visual analytics techniques can be used in facial recognition thus enabling fine-grain user tracking. This paper addresses such privacy concerns for some specific scenarios. It explores the feasibility of visual analytics in using low-cost/low-resolution thermal cameras thus delivering context-awareness information yet protecting user's privacy. This paper proposes a model able to classify and count humans, in indoor hallway settings, using low-resolution thermal pictures. The model is designed to work with YOLOv3 and leverages the power of deep-learning. Results show that it is possible to classify and count humans with over 90% accuracy based on the images from a low-cost 80x60 pixel thermal camera. The results were evaluated against the ground truth checked by a human agent and recorded through a regular camera. The study exposed possibilities and limits offered by low-cost thermal cameras and identifies the potential application scenarios. The dataset including both real and thermal images used for the training and the testing will be made available to the scientific community.

Original languageEnglish
Title of host publicationTwelfth International Conference on Digital Image Processing, ICDIP 2020
EditorsXudong Jiang, Hiroshi Fujita
ISBN (Electronic)9781510638457
Publication statusPublished - 2020
Event12th International Conference on Digital Image Processing, ICDIP 2020 - Osaka, Japan
Duration: 19 May 202022 May 2020

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
ISSN (Print)0277-786X
ISSN (Electronic)1996-756X


Conference12th International Conference on Digital Image Processing, ICDIP 2020


  • Cnn
  • Human detection
  • Privacy-friendly
  • Thermal camera
  • Yolov3


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