MmWave radar point cloud segmentation using gmm in multimodal traffic monitoring

Feng Jin, Arindam Sengupta, Siyang Cao, Yao Jan Wu

Research output: Contribution to journalArticlepeer-review

Abstract

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.

Original languageEnglish (US)
JournalUnknown Journal
StatePublished - Nov 14 2019

Keywords

  • Classification
  • Gaussian mixture model
  • Mmwave radar
  • Radar point cloud
  • Segmentation
  • Traffic monitoring

ASJC Scopus subject areas

  • General

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