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Related Experiment Video

Updated: Dec 13, 2025

Using a Virtual Reality Walking Simulator to Investigate Pedestrian Behavior
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A Coarse-to-Fine Framework for Multiple Pedestrian Crossing Detection.

Yuhua Fan1,2, Zhonggui Sun1, Guoying Zhao2

  • 1School of Mathematical Science, Liaocheng University, Liaocheng 252000, China.

Sensors (Basel, Switzerland)
|July 30, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces a new method to detect pedestrian crossings using probe vehicle videos. The approach improves the utilization of video data for mapping pedestrian crossing locations.

Keywords:
coarse-to-finedetectionpedestrian crossingprobe vehicle video

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Area of Science:

  • Computer Vision
  • Geographic Information Systems
  • Transportation Engineering

Background:

  • Pedestrian safety is paramount in route guidance, necessitating accurate mapping of pedestrian crossings.
  • Current methods underutilize video data by only detecting crossings directly in front of the camera.
  • Map service providers require comprehensive pedestrian crossing data for road networks.

Purpose of the Study:

  • To develop a robust framework for detecting pedestrian crossings from probe vehicle videos.
  • To enhance the utilization of visual information for mapping pedestrian crossing locations.
  • To improve the accuracy and completeness of pedestrian crossing data in road networks.

Main Methods:

  • A coarse-to-fine framework is proposed for pedestrian crossing detection.
  • The coarse stage identifies vanishing points and lines to define candidate regions of interest (ROIs).
  • The fine stage uses prior constraints to validate candidate ROIs as pedestrian crossings.

Main Results:

  • The framework effectively detects pedestrian crossings from probe vehicle videos.
  • The method combines visual data with GPS traces for precise location determination.
  • Field experiments in Beijing and Shanghai demonstrated satisfactory performance across diverse scenarios.

Conclusions:

  • The proposed coarse-to-fine framework significantly improves pedestrian crossing detection from probe vehicle videos.
  • This approach enables more comprehensive mapping of pedestrian crossings, enhancing road network data.
  • The method offers a practical solution for map service providers to gather crucial pedestrian safety infrastructure information.