Calibration of camera internal parameters based on grey wolf optimization improved by levy flight and mutation

Area of Science:

• Computer Vision
• Optimization Algorithms
• Metrology

Background:

• Traditional camera calibration methods face accuracy limitations in demanding applications.
• Achieving high precision in measurement and monitoring is critical across various scientific fields.

Purpose of the Study:

• To develop an advanced optimization algorithm for accurate camera internal parameter estimation.
• To address the shortcomings of conventional calibration techniques in achieving required accuracy.

Main Methods:

• Proposed a Grey Wolf Optimizer incorporating Levy flight and a mutation mechanism.
• Utilized a nonlinear model with minimum average reprojection error as the objective function.
• Iteratively calculated optimal camera internal parameters through grey wolf position optimization.

Main Results:

• Experimental validation using two datasets demonstrated the algorithm's effectiveness.
• The proposed method significantly improved calibration accuracy over other optimization techniques.
• Achieved superior performance in estimating camera internal parameters.

Conclusions:

• The Grey Wolf Optimizer with Levy flight and mutation offers a robust solution for camera calibration.
• This approach enhances precision for measurement and monitoring tasks requiring high accuracy.
• The algorithm provides a significant advancement in optical metrology and computer vision applications.