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A Trinocular System for Pedestrian Localization by Combining Template Matching with Geometric Constraint

Jinjing Zhao1, Sen Huang1, Yancheng Li1

  • 1School of Electronics, Peking University, Beijing 100871, China.

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|October 16, 2025
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This study introduces a novel trinocular stereo vision system for accurate pedestrian localization. The framework enhances depth estimation and achieves real-time performance, outperforming existing binocular methods.

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pedestrian localizationtemplate matchingtrinocular vision

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Area of Science:

  • Computer Vision
  • Robotics
  • Sensor Fusion

Background:

  • Pedestrian localization is crucial for intelligent outdoor systems.
  • Conventional binocular stereo vision methods face limitations in accuracy and efficiency.
  • Advanced sensing is required for reliable outdoor navigation and safety.

Purpose of the Study:

  • To develop an improved pedestrian localization system using trinocular stereo vision.
  • To enhance depth estimation accuracy and processing efficiency compared to binocular approaches.
  • To enable real-time pedestrian detection and tracking in outdoor environments.

Main Methods:

  • A trinocular camera configuration in an equilateral triangle setup for complementary perspectives.
  • Integration of multi-scale template matching for initial depth estimation.
  • Enforcement of three-view geometric consistency for refined depth estimates.
  • Combination with YOLOv8-s detector for object detection and localization.

Main Results:

  • Achieved a mean absolute error of 0.435 m for pedestrian depth estimation.
  • Demonstrated an average processing time of 3.13 ms per target, surpassing binocular methods.
  • Real-time operation at >30 Hz with end-to-end latency of ~32.56 ms for up to nine pedestrians.

Conclusions:

  • The proposed trinocular stereo vision framework significantly improves pedestrian localization accuracy and efficiency.
  • The system offers a robust solution for real-time outdoor intelligent systems.
  • This approach provides a foundation for enhanced autonomous navigation and safety systems.