MmWave Radar Point Cloud Segmentation using GMM in Multimodal Traffic Monitoring
This work addresses the challenge of sparse radar data for multimodal traffic monitoring, which is incremental as it applies an existing method (GMM) with a new feature vector to improve segmentation in a specific domain.
The paper tackled the problem of classifying transportation modes like pedestrians and cars in traffic monitoring by using a high-resolution mmWave radar to obtain richer point clouds and applying a multivariate Gaussian mixture model (GMM) for unsupervised segmentation, achieving good performance as measured by intersection-over-union (IoU) metrics.
In multimodal traffic monitoring, we gather traffic statistics for distinct transportation modes, such as pedestrians, cars and bicycles, in order to analyze and improve people's daily mobility in terms of safety and convenience. On account of its robustness to bad light and adverse weather conditions, and inherent speed measurement ability, the radar sensor is a suitable option for this application. However, the sparse radar data from conventional commercial radars make it extremely challenging for transportation mode classification. Thus, we propose to use a high-resolution millimeter-wave(mmWave) radar sensor to obtain a relatively richer radar point cloud representation for a traffic monitoring scenario. Based on a new feature vector, we use the multivariate Gaussian mixture model (GMM) to do the radar point cloud segmentation, i.e. `point-wise' classification, in an unsupervised learning environment. In our experiment, we collected radar point clouds for pedestrians and cars, which also contained the inevitable clutter from the surroundings. The experimental results using GMM on the new feature vector demonstrated a good segmentation performance in terms of the intersection-over-union (IoU) metrics. The detailed methodology and validation metrics are presented and discussed.