CIM dataset

Traffic monitoring systems featuring robust, multi-sensor fusion capabilities are rapidly
growing in demand to observe traffic flow, reduce congestion and to detect and report traffic accidents.
However, monitoring outdoor environments using cameras remains challenging due to complex weather
conditions, including fog, rain, snow and variable lighting conditions. The presence of these weather
conditions can significantly reduce vehicle detection and classification performance using machine learn-
ing methods. Unfortunately, openly available datasets for multi-sensor traffic monitoring development
and testing remain limited, especially those featuring infrastructure-based cameras and millimeter wave
(mmWave) radar. To address these challenges, we evaluate open camera and mmWave radar data using
vehicle classification models for cars, trucks, vans and buses on embedded hardware. We also provide an
open multi-sensor traffic monitoring dataset with more than 8,000 manually annotated frames as well as
mmWave radar point clouds recorded in an urban environment under sunny, partially cloudy, cloudy, rainy
and night conditions.