Monitoring vehicle flows in cities is crucial to improve the urban
environment and quality of life of citizens. Images are the best sensing
modality to perceive and assess the flow of vehicles in large areas. Current
technologies for vehicle counting in images hinge on large quantities of
annotated data, preventing their scalability to city-scale as new cameras are
added to the system. This is a recurrent problem when dealing with physical
systems and a key research area in Machine Learning and AI. We propose and
discuss a new methodology to design image-based vehicle density estimators with
few labeled data via multiple camera domain adaptations.Comment: 1st International Workshop on New Foundations for Human-Centered AI
(NeHuAI) at ECAI-202
