Social Distance
Social distance monitoring aims to automatically detect and measure the proximity between individuals, primarily driven by the need for public health management and safety. Current research focuses on developing robust and efficient methods using various sensor technologies, including millimeter-wave radar, low-resolution infrared sensors, and computer vision with deep learning models like YOLOv5 and CNNs. These approaches are being evaluated across diverse settings, from public transportation to general indoor and outdoor spaces, with a strong emphasis on balancing accuracy with privacy and energy efficiency. The resulting technologies offer potential for improved pandemic response, enhanced workplace safety, and more effective management of crowded environments.
Papers
Privacy-preserving Social Distance Monitoring on Microcontrollers with Low-Resolution Infrared Sensors and CNNs
Chen Xie, Francesco Daghero, Yukai Chen, Marco Castellano, Luca Gandolfi, Andrea Calimera, Enrico Macii, Massimo Poncino, Daniele Jahier Pagliari
Energy-efficient and Privacy-aware Social Distance Monitoring with Low-resolution Infrared Sensors and Adaptive Inference
Chen Xie, Daniele Jahier Pagliari, Andrea Calimera