Detection and tracking of pedestrians and vehicles using roadside LiDAR sensors

Junxuan Zhao, Hao Xu, Hongchao Liu, Jianqing Wu, Yichen Zheng, Dayong Wu

Research output: Contribution to journalArticlepeer-review

87 Scopus citations


Light Detection and Ranging (LiDAR) is a remote sensing technology widely used in many areas ranging from making precise medical equipment to creating accurate elevation maps of farmlands. In transportation, although it has been used to assist some design and planning works, the application has been predominantly focused on autonomous vehicles, regardless of its great potential in precise detection and tracking of all road users if implemented in the field. This paper explores fundamental concepts, solution algorithms, and application guidance associated with using infrastructure-based LiDAR sensors to accurately detect and track pedestrians and vehicles at intersections. Based on LiDAR data collected in the field, investigations were conducted in the order of background filtering, object clustering, pedestrian and vehicle classification, and tracking. The results of the analysis include accurate and real-time information of the presence, position, velocity, and direction of pedestrians and vehicles. By studying the data from infrastructure-mounted LiDAR sensors at intersections, this paper offers insights into some critical techniques that are valuable to both researchers and practitioners toward field implementation of LiDAR sensors.

Original languageEnglish
Pages (from-to)68-87
Number of pages20
JournalTransportation Research Part C: Emerging Technologies
StatePublished - Mar 2019


  • Classification
  • Clustering
  • Infrastructure-based LiDAR
  • Pedestrians and Vehicles
  • Trajectory Extraction


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