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Automatic Calibration of an Around View Monitor System Exploiting Lane Markings.

Kyoungtaek Choi1, Ho Gi Jung2, Jae Kyu Suhr3

  • 1Department of Electronic Engineering, Korea National University of Transportation, 50 Daehak-ro, Chungju-si, Chungbuk 27469, Korea. maninquestion75@gmail.com.

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Summary
This summary is machine-generated.

This study presents an automatic camera calibration method for around view monitor (AVM) systems using lane markings. The approach ensures accurate orientation angles for seamless, real-world driving visualizations.

Keywords:
advanced driver assistance system (ADAS)around view monitor (AVM) systemautomatic calibrationlane markingparking assist system

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Area of Science:

  • Computer Vision
  • Robotics
  • Automotive Engineering

Background:

  • Around View Monitor (AVM) systems enhance vehicle safety and situational awareness.
  • Accurate camera calibration is crucial for seamless AVM image stitching.
  • Existing calibration methods often require controlled environments or manual intervention.

Purpose of the Study:

  • To propose an automatic camera calibration method for AVM systems in natural driving conditions.
  • To estimate the orientation angles of four AVM cameras using lane markings.
  • To achieve accurate and seamless AVM image generation without manual setup.

Main Methods:

  • Lane marking detection from four AVM camera images in a cost-effective manner.
  • Statistical analysis to reject false lane markings.
  • A two-step calibration process: front/rear cameras first, then left/right cameras.
  • Refinement of calibration using corresponding lane markings across adjacent cameras.
  • Iterative calibration with medoid selection for robustness and efficiency.

Main Results:

  • Successful automatic estimation of camera orientation angles in real driving scenarios.
  • Demonstrated ability to generate seamless AVM images through accurate calibration.
  • Quantitative and qualitative evaluation showing promising performance across various driving situations.

Conclusions:

  • The proposed method offers an effective solution for automatic AVM camera calibration in natural driving.
  • Utilizing lane markings provides a robust and widely applicable calibration strategy.
  • The two-step approach and iterative refinement ensure high accuracy and seamless visualization.