Empirical study evaluates AI vision-based smart sensor for vehicle counting, indicating reliable performance in urban settings.
Accurate vehicle counting is essential for urban mobility and environmental health applications, including pollution modeling, congestion analysis, and traffic simulation. This paper presents an empirical study conducted in Marrakech evaluating an AI vision-based smart sensor built on a low-cost Jetson Orin Nano. Our results show that a software pipeline combining a frame grabber, video strider, YOLO12n detector, OcSORT tracker, and a line-vicinity counter delivers reliable, real-time vehicle counting performance.
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Elouiaazzani et al. (2026) studied this question.
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