TY - GEN
T1 - mmwave radar point cloud segmentation using GMM in multimodal traffic monitoring
AU - Jin, Feng
AU - Sengupta, Arindam
AU - Cao, Siyang
AU - Wu, Yao Jan
N1 - Publisher Copyright:
© 2020 IEEE
PY - 2020/4
Y1 - 2020/4
N2 - 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.
AB - 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.
KW - Classification
KW - Gaussian mixture model
KW - Mmwave radar
KW - Radar point cloud
KW - Segmentation
KW - Traffic monitoring
UR - http://www.scopus.com/inward/record.url?scp=85090325397&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85090325397&partnerID=8YFLogxK
U2 - 10.1109/RADAR42522.2020.9114662
DO - 10.1109/RADAR42522.2020.9114662
M3 - Conference contribution
AN - SCOPUS:85090325397
T3 - 2020 IEEE International Radar Conference, RADAR 2020
SP - 732
EP - 737
BT - 2020 IEEE International Radar Conference, RADAR 2020
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2020 IEEE International Radar Conference, RADAR 2020
Y2 - 28 April 2020 through 30 April 2020
ER -