A Novel Central Camera Calibration Method Recording Point-to-Point Distortion for Vision-Based Human Activity...

Area of Science:

• Computer Vision
• Robotics
• Image Processing

Background:

• Camera calibration is crucial for vision-based human activity recognition.
• Multi-parameter models offer high accuracy but require extensive image datasets.
• Existing methods are computationally intensive and impractical for real-time applications.

Purpose of the Study:

• To develop an efficient camera distortion calibration method.
• To reduce the number of images required for accurate calibration.
• To improve the precision of camera distortion rectification.

Main Methods:

• A novel point-to-point camera distortion calibration technique.
• An objective function minimizing deformation between original and projected reference images.
• Digital Image Correlation (DIC) for dense feature matching.

Main Results:

• Achieved comparable results to multi-parameter methods using significantly fewer images.
• Obtained a dense distortion rectification map with dozens of images.
• Demonstrated a 28.5% improvement in reprojection error over polynomial distortion models.

Conclusions:

• The proposed method offers an efficient and accurate solution for camera distortion calibration.
• It significantly reduces the data requirements for high-precision calibration.
• This advancement is vital for practical applications in human activity recognition and computer vision.