🤖 AI Summary
Accurate calibration of multi-camera systems remains challenging under realistic constraints such as the absence of calibration boards, inaccessible scenes, and unsynchronized video streams. To address this, we propose a two-stage calibration method that requires no physical calibration patterns. In the first stage, intrinsic parameters and effective field-of-view (EFOV) are estimated from a single static image per camera by manually annotating geometric primitives—namely, parallel, orthogonal, and equal-length line segments—exploiting natural scene geometry. In the second stage, extrinsic parameters are recovered via interactive EFOV plane projection and alignment with virtual calibration elements, enabling calibration without a physical planar target. The method significantly enhances flexibility and applicability, requiring only one static image per camera. Experimental results demonstrate superior accuracy and robustness compared to conventional calibration approaches. It has been successfully deployed in complex real-world scenarios for multi-camera collaborative tracking.
📝 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.