SOCIAL DISTANCING USING COMPUTER VISION AND DEEP LEARING
Keywords:
Social Distancing, Deep Learning, YOLOv8, COCO, OpenVINO, Abbreviations and Acronyms, COCO-Common Objects in Context, OpenVINO-Open Visual Inference and Neural Network OptimizationAbstract
Social distancing is a critical measure in containing the spread of Covid-19, even with the availability of
effective vaccines. To ensure the maximum reduction of virus transmission and minimize its impact,
adhering to social distancing norms is imperative. In this study, we propose a Python-based deep learning
approach using the YOLOv8 model trained on the COCO dataset to monitor social distancing in public
spaces. Our software tool analyzes real-time video streams from CC cameras and employs OpenVINO
inference for efficient and accelerated model deployment. By leveraging YOLOv8 with OpenVINO, we can
accurately detect and monitor individuals' compliance with proper social distancing practices.
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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
