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Updated: Jan 11, 2026

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Real-Time Robust 2.5D Stereo Multi-Object Tracking with Lightweight Stereo Matching Algorithm.

Jinhyeong Lee1, Junyoung Shin1, Eunwoo Park1,2

  • 1Department of Mechanical Engineering, Dankook University, Yongin 16890, Republic of Korea.

Sensors (Basel, Switzerland)
|November 13, 2025
PubMed
Summary
This summary is machine-generated.

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This study introduces StereoSORT, a real-time stereo tracking system that overcomes occlusion challenges using lightweight geometric constraints. It achieves high accuracy and speed, offering a practical solution for resource-limited environments.

Area of Science:

  • Computer Vision
  • Robotics
  • Artificial Intelligence

Background:

  • Monocular vision systems struggle with object tracking due to occlusions and truncations.
  • Existing stereo vision methods for tracking are computationally intensive, requiring dense matching or 3D reconstruction.
  • There is a need for efficient and accurate stereo multi-object tracking solutions.

Purpose of the Study:

  • To develop a real-time 2.5D stereo multi-object tracking framework.
  • To improve tracking robustness against occlusions using geometric constraints.
  • To provide a computationally efficient alternative to existing stereo tracking methods.

Main Methods:

  • Implemented a lightweight stereo matching module using Direct Linear Transform-based triangulation with bounding box coordinates.
Keywords:
depth estimationmulti-object trackingocclusion & truncation handlingstereo matchingstereo trackingstereo visiontracker re-identification

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  • Utilized a dual-tracker architecture for independent view tracking and re-identification.
  • Employed geometric constraints for robust correspondence without appearance features.

    • Main Results:

    • StereoSORT achieved a Multiple Object Tracking Accuracy (MOTA) of 0.932 and an Identification F1 score (IDF1) of 0.823.
    • Outperformed monocular trackers like OC-SORT (IDF1: 0.765) and ByteTrack (IDF1: 0.609).
    • Achieved a median depth error of 50.1 mm and a processing speed of 70 FPS.

    Conclusions:

    • Geometric constraints alone are sufficient for robust stereo tracking, eliminating the need for costly appearance features.
    • StereoSORT offers a practical and efficient solution for multi-object tracking in resource-constrained environments.
    • The framework demonstrates high accuracy and reliability comparable to commercial sensors.