Privacy-Preserved Social Distancing System Using Low-Resolution Thermal Sensors and Deep Learning

During the current COVID-19 pandemic, it was obvious that life slowed down, and people became less productive with remote working through online platforms. It has become a necessity to find solutions to facilitate our daily routines such as going to school, work and travel while abiding by the guideline for social distancing and cope with the existence of COVID-19 or any upcoming health pandemics. We employed an IoT and deep learning method to propose an indoor privacy-preserved social distancing wireless sensor network-based system using 8×8 low-resolution infrared sensor AMG8833, which can be used by companies and organizations to ensure that social distancing is practiced at all time. To the best of our knowledge, there is no practical privacy-preserved technology in the market. We collected a total of 6606 infrared low-resolution images using four wireless sensor nodes to cover as many as 200 cases. We employed YOLOv4-tiny object detection model for detecting persons which is trained on our dataset, and achieved mAP@0.5 equal to 95.4% and inference time of 5.16ms. Thus, we conclude our proposed novel social distancing system has a potential in fighting COVID19.

Experimental result

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