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In multimodal traffic monitoring, we gather traffic statistics for distincttransportation modes, such as pedestrians, cars and bicycles, in order toanalyze and improve people's daily mobility in terms of safety and convenience.On account of its robustness to bad light and adverse weather conditions, andinherent speed measurement ability, the radar sensor is a suitable option forthis application. However, the sparse radar data from conventional commercialradars make it extremely challenging for transportation mode classification.Thus, we propose to use a high-resolution millimeter-wave(mmWave) radar sensorto obtain a relatively richer radar point cloud representation for a trafficmonitoring scenario. Based on a new feature vector, we use the multivariateGaussian mixture model (GMM) to do the radar point cloud segmentation, i.e.`point-wise' classification, in an unsupervised learning environment. In ourexperiment, we collected radar point clouds for pedestrians and cars, whichalso contained the inevitable clutter from the surroundings. The experimentalresults using GMM on the new feature vector demonstrated a good segmentationperformance in terms of the intersection-over-union (IoU) metrics. The detailedmethodology and validation metrics are presented and discussed.

MmWave Radar Point Cloud Segmentation using GMM in Multimodal Traffic Monitoring