一种无需离线校准的多摄像头跟踪方法
On-the-Fly Homographies Calibration for Multi-Camera Tracking
这个方法很实用，它解决了多摄像头跟踪中一个常见的问题，就是传统方法需要离线校准，而新方法可以在运行时自动调整，不需要人工干预，而且对隐私很友好。
该研究提出了一种通过实时几何优化来校准多摄像头的方法，从粗略的手动校准开始，使用基于质心的投影优化（PO）持续对地面平面几何进行对齐。由于PO在已传输的轻量级元数据上异步运行，因此不会增加实时跟踪器的计算延迟。这使系统能够自动适应摄像头移动或环境变化，而无需人工干预。优化后的几何形状为多摄像头鸟瞰图（BEV）跟踪器提供输入，该跟踪器融合检测并统一跨区域轨迹。
On-the-Fly Homographies Calibration for Multi-Camera Tracking
Precise multi-camera tracking traditionally relies on rigorous 3D site calibration, yet this requirement is often operationally impossible in large-scale deployments. Privacy regulations frequently prohibit recording video for offline calibration; limited bandwidth precludes synchronizing high-resolution streams from hundreds of cameras; and covering immense physical sites with calibration targets is logistically infeasible. We present a multi-camera homography calibration system designed to overcome these barriers through "on-the-fly" geometric refinement. Starting from coarse manual homographies, we introduce a centroid-based projection optimization (PO) that continuously aligns the ground-plane geometry using live detection streams. Because PO operates asynchronously on already-transmitted, lightweight metadata, it adds zero computational latency to the real-time tracker. This allows the system to adapt automatically to camera movements or environmental changes without human intervention. This optimized geometry feeds a multi-camera bird's-eye-view (BEV) tracker that fuses detections and unifies trajectories across zones. Crucially, by operating strictly on live anonymous metadata, our solution ensures a privacy-safe, zero-overhead, and resilient tracking pipeline that maintains global consistency in dynamic environments where static, recorded-video calibration is impossible.