Open webcam data for traffic monitoring: YOLOv8 detection of road users before and during COVID-19

Abstract

Traffic volumes are rising globally, creating a growing need for accurate and scalable data collection to address mobility challenges and enhance transport systems. Yet, traditional methods remain costly and time-consuming despite advances in automated monitoring. This study explores the feasibility of using open webcam data in combination with the state-of-the-art object detection model YOLOv8 out-of-the-box for road user monitoring. Publicly accessible webcam imagery presents challenges such as high variability in image quality, road user occlusion, and environmental factors like poor visibility due to weather conditions. To assess their potential for traffic monitoring, we utilize open webcam data from Germany to evaluate the performance of YOLOv8′s model variants, testing 110 parameter combinations with a manually labeled reference dataset. Among the tested out-of-the-box model variants, YOLOv8x achieved the highest performance, with an F1-score of 0.75. This optimized model was applied to about 500,000 open webcam scenes to monitor the change of road users before and during the COVID-19 pandemic. The analysis revealed a 9.5% overall reduction in road users volume, with motorized road users declining significantly while bicycles increased by 25.2%. This reflects mobility patterns observed during the COVID-19 pandemic, where restrictions led to a significant shift towards cycling as an alternative mode of transport. The results are plausible as they mirror broader trends in active mobility observed in various urban contexts. Our findings demonstrate the potential of leveraging open webcam data and pre-trained object detection models for scalable, cost-effective transport monitoring.