Two-Stage Camera Calibration Method for Multi-Camera Systems Using Scene Geometry

TL;DR

This paper introduces a two-stage, scene-geometry-based camera calibration method for multi-camera systems that requires only static images, enabling accurate tracking without physical access or synchronized videos.

Contribution

A novel calibration approach that combines partial operator-guided annotation with interactive alignment, eliminating the need for physical calibration patterns or synchronized video streams.

Findings

Method achieves high calibration accuracy in real-world scenarios.

Requires only static images, no physical access or synchronized videos.

Validated through comparative analysis and demonstration videos.

Abstract

Calibration of multi-camera systems is a key task for accurate object tracking. However, it remains a challenging problem in real-world conditions, where traditional methods are not applicable due to the lack of accurate floor plans, physical access to place calibration patterns, or synchronized video streams. This paper presents a novel two-stage calibration method that overcomes these limitations. In the first stage, partial calibration of individual cameras is performed based on an operator's annotation of natural geometric primitives (parallel, perpendicular, and vertical lines, or line segments of equal length). This allows estimating key parameters (roll, pitch, focal length) and projecting the camera's Effective Field of View (EFOV) onto the horizontal plane in a base 3D coordinate system. In the second stage, precise system calibration is achieved through interactive manipulation of the projected EFOV polygons. The operator adjusts their position, scale, and rotation to align them with the floor plan or, in its absence, using virtual calibration elements projected onto all cameras in the system. This determines the remaining extrinsic parameters (camera position and yaw). Calibration requires only a static image from each camera, eliminating the need for physical access or synchronized video. The method is implemented as a practical web service. Comparative analysis and demonstration videos confirm the method's applicability, accuracy, and flexibility, enabling the deployment of precise multi-camera tracking systems in scenarios previously considered infeasible.