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Cameras are becoming more pervasive and ubiquitous. The daily activities of individuals are being captured by millions of cameras in public spaces, while individuals are obtaining massive amounts of egocentric videos by employing wearable cameras intended for life-logging. However, recording devices are inexpensive, highly computational, and inconvenient for privacy. We used a low-resolution infrared sensor to detect human activity, including sitting, standing, and lying down, and to locate humans. We acquired the data from a low-cost infrared device and preprocessed them to train the YOLO-v5 network. We developed and tested an infrared technology-based system consisting of thermal input. Our proposed model is trained on 3,864 low-resolution images and made publicly available. The trained YOLO-v5 achieved 96.34% mean Average Precision (mAP) using our designed lightweight and low …