Privacy aware crowd-counting using thermal cameras

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.