mmwave radar point cloud segmentation using GMM in multimodal traffic monitoring

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

37 Scopus citations

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)
Title of host publication2020 IEEE International Radar Conference, RADAR 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages732-737
Number of pages6
ISBN (Electronic)9781728168128
DOIs
StatePublished - Apr 2020
Externally publishedYes
Event2020 IEEE International Radar Conference, RADAR 2020 - Washington, United States
Duration: Apr 28 2020Apr 30 2020

Publication series

Name2020 IEEE International Radar Conference, RADAR 2020

Conference

Conference2020 IEEE International Radar Conference, RADAR 2020
Country/TerritoryUnited States
CityWashington
Period4/28/204/30/20

Keywords

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

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Signal Processing
  • Instrumentation

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