Traffic density estimation via unsupervised domain adaptation

Monitoring traffic flows in cities is crucial to improve urban mobility, and images are the best sensing modality to perceive and assess the flow of vehicles in large areas. However, current machine learning-based technologies using images hinge on large quantities of annotated data, preventing their scalability to city-scale as new cameras are added to the system. We propose a new methodology to design image-based vehicle density estimators with few labeled data via an unsupervised domain adaptation technique.