GRACE：几何与射线感知的相机高效多视角行人跟踪
GRACE: Geometry- and Ray-Aware Camera-Efficient Multi-View Pedestrian Tracking
- Meijo University（名城大学）
- Chubu Electric Power Co., Inc.（中部电力公司）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
GRACE通过体积引导融合、射线条件化和BEV轨迹恢复，在减少相机数量下提升多视角行人跟踪性能，MOTA从83.54升至91.07。
AI中文摘要：
减少相机数量可降低部署成本，但会移除那些通过投影纠正BEV响应偏离真实行人位置的视角，以及可能分割轨迹的短暂分数下降。我们提出GRACE，一种相机高效的多视角跟踪器，包含三个组件。体积引导融合将基于单应性的BEV特征与通过3D空间提升的特征相结合。射线条件化将每个相机的观察方向暴露给融合网络。其跟踪组件BEV轨迹恢复（BTR）仅使用低置信度检测来延续现有轨迹。相同的检测不能启动新轨迹。使用两台WildTrack相机，GRACE将MOTA从我们基线TrackTacular的83.54提升到91.07。
英文摘要：
Reducing the number of cameras reduces the deployment cost but removes views that correct BEV responses stretched away from true pedestrian positions by projection and short score drops that can split tracks} in Bird's-Eye View (BEV) tracking. We introduce GRACE, a camera-efficient multi-view tracker with three components. Volumetric-Guided Fusion combines homography-based BEV features with features lifted through 3D space. Ray Conditioning exposes each camera's viewing direction to the fusion network. Its tracking component, BEV Track Recovery (BTR), uses low-confidence detections only to continue existing tracks. The same detections cannot start new tracks. With two WildTrack cameras, GRACE improves MOTA from 83.54 for TrackTacular, our baseline, to 91.07.
