多摄像头跟踪的即时单应性校准

中文摘要 AI 辅助

精确的多摄像头跟踪传统上依赖于严格的3D场地校准，然而在大规模部署中，这一要求通常在操作上难以实现。隐私法规常常禁止录制视频用于离线校准；有限的带宽妨碍了从数百个摄像头同步传输高分辨率流；而用校准目标覆盖巨大的物理场地在物流上不可行。我们提出了一种多摄像头单应性校准系统，旨在通过“即时”几何精化来克服这些障碍。从粗略的手动单应性开始，我们引入了一种基于质心的投影优化（PO），利用实时检测流持续对齐地面平面几何。由于PO异步操作于已传输的轻量级元数据上，它为实时跟踪器增加了零计算延迟。这使得系统能够自动适应摄像头移动或环境变化，无需人工干预。这种优化的几何信息馈送给一个多摄像头鸟瞰图（BEV）跟踪器，该跟踪器融合检测结果并统一跨区域的轨迹。关键在于，通过严格操作于实时匿名元数据，我们的解决方案确保了隐私安全、零开销且具有弹性的跟踪流程，在静态、录制视频校准不可能的动态环境中维持全局一致性。

英文摘要

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.

发表机构

• Juganu
• Tel Aviv University（特拉维夫大学）

机构由 AI 辅助整理，请以论文原文为准。