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A dense background representation method for traffic surveillance based on roadside LiDAR

  • Published:2022-05 Issue: Volume:152 Page:106982
  • ISSN:0143-8166
  • Container-title:Optics and Lasers in Engineering
  • language:en
  • Short-container-title:Optics and Lasers in Engineering

Author:

Xia Yingji,Sun Zhe,Tok Andre,Ritchie Stephen

Publisher

Elsevier BV

Subject

Electrical and Electronic Engineering,Mechanical Engineering,Atomic and Molecular Physics, and Optics,Electronic, Optical and Magnetic Materials

Reference41 articles.

1. A robust approach for road users classification using the motion cues;Talib;Transp Res Part Emerg Technol,2016

2. Camera calibration and vehicle tracking: highway traffic video analytics;Wan;Transp Res Part C Emerg Technol,2014

3. Traffic speed estimation from surveillance video data;Huang,2018

4. A convolutional neural-network-based pedestrian counting model for various crowded scenes;Shen;Comput Aided Civ Infrastruct Eng,2019

5. Automatic background construction and object detection based on roadside LiDAR;Zhang;IEEE Trans Intell Transp Syst,2020

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