CB-OWL-ViT: A Multimodal Cost-Effective Framework for Contagious Disease Monitoring

The rapid spread of diseases like COVID-19 highlights the need for adaptable monitoring systems to support public health measures such as mask compliance and social distancing. This study presents the CB-OWL-ViT framework: a Cluster-Based Open-World Localization Vision Transformer for mask detection and social distance estimation. It incorporates homography-based distance estimation for effective deployment with monocular cameras. The innovative integration of open-world vision-language detection with a clustering-based strategy enhances mask-wearing assessments, enabling adaptability without retraining. Evaluations on datasets including Kaggle, Roboflow, and a new dataset from the University of Waterloo show that CB-OWL-ViT improves mask detection precision by 0.37 and F1-score by 0.2 compared to the baseline. The homography module achieves a Mean Absolute Error of 0.1116 in distance estimation, and real-world tests demonstrate a recall of 0.98 for detecting noncompliance in the “Without Mask” class. This framework is a practical solution for large-scale disease monitoring across various settings.