发表机构
Meijo University; Chubu Electric Power Co., Inc.（名城大学; 中部电力公司）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
CAT-Free提出无需标定、标注或目标场景训练的多视角行人定位方法，通过自适应几何滤波去除不可靠估计，在多个数据集上取得良好性能并支持无调整迁移。
AI 中文摘要
多相机行人定位对于公共和商业空间中的广域监控非常有用。然而，为每个新环境部署这些系统通常需要大量的设置工作。现有方法通常需要相机标定、位置标注或目标场景训练。CAT-Free消除了这三个要求。它仅使用同步的RGB视频作为场景特定输入。相机配置直接从视频中估计。然后通过结合多个相机的观测来估计行人位置。自动相机估计并不总是准确的。这可能会产生不可靠的行人位置。因此，CAT-Free引入了两个自适应几何滤波器。它们移除不可靠的位置估计。它们的阈值从每个输入序列中估计。CAT-Free在WildTrack、MultiviewX和GMVD上分别达到82.5、84.5和65.7的MODA。它不使用提供的标定、位置标注或目标场景训练。使用此类场景特定信息的已发表方法在各自协议下报告WildTrack上的MODA为88.2--95.0，MultiviewX上为83.9--96.5。CAT-Free还可以无需重新调整地迁移。它在四个额外序列上达到74.9的MODA，在一个未见过的8相机安装上达到78.6。最后，定位不确定性以r=-0.98预测MODA。这提供了无标签的定位可靠性估计。
英文摘要
Multi-camera pedestrian localization is useful for wide-area monitoring in public and commercial spaces. However, deploying these systems often requires considerable setup for each new environment. Existing methods typically require camera calibration, position annotations, or target-scene training. CAT-Free removes all three requirements. It uses synchronized RGB video as its only scene-specific input. Camera configuration is estimated directly from the video. Pedestrian locations are then estimated by combining observations from multiple cameras. Automatic camera estimation is not always accurate. This can produce unreliable pedestrian locations. CAT-Free therefore introduces two adaptive geometric filters. They remove unreliable position estimates. Their thresholds are estimated from each input sequence. CAT-Free achieves 82.5, 84.5, and 65.7 MODA on WildTrack, MultiviewX, and GMVD. It uses no supplied calibration, position annotations, or target-scene training. Published methods using such scene-specific information report 88.2--95.0 MODA on WildTrack and 83.9--96.5 on MultiviewX under their respective protocols. CAT-Free also transfers without retuning. It reaches 74.9 MODA on four additional sequences and 78.6 on an unseen 8-camera installation. Finally, localization uncertainty predicts MODA with $r=-0.98$. This provides a label-free estimate of localization reliability.
