外观
论文arXiv 2610.088261 个月前
PanoPed：超越边界框的 Sim-to-Real 全景行人跟踪
原文链接：PanoPed: Beyond Bounding Boxes for Sim-to-Real Panoramic Pedestrian Tracking（arXiv 2610.08826 · 发布日期：2026-09-25）
PanoPed: Beyond Bounding Boxes for Sim-to-Real Panoramic Pedestrian Tracking
PanoPed提出全球面行人跟踪基准与Sextant角定位头，在合成与真实全景视频上验证提升跟踪效果。
核心要点
- 全球面相机可全向跟踪行人，但平面框不能完整描述球面位置。
- PanoPed-S含仿真多平台数据，PanoPed-R含固定相机真实帧及密集标注。
- Sextant为即插即用角定位头，复用冻结检测器且不需额外图像编码器。
- Sextant在PanoPed-S最佳，并在无真实微调下提升真实视频HOTA。
验证范围与局限：验证范围包括PanoPed-S的固定、四足和无人机相机仿真数据，以及PanoPed-R固定相机真实视频。
英文摘要 (Abstract)
Full-sphere panoramic cameras let fixed monitoring systems and mobile robots track people in every direction, but a planar bounding box does not fully describe where a person is on the sphere. We introduce PanoPed, a sim-to-real benchmark for pedestrian tracking on the full sphere. PanoPed-S contains 108,000 frames from fixed, quadruped-mounted, and drone-mounted cameras, with synchronized masks, depth, camera poses, and 3D pedestrian states. PanoPed-R adds 28,002 real frames from fixed cameras, 16,247 of them densely annotated. We find that an ERP rectangle cannot uniquely determine the spherical center and angular extent of the visible person, while the detector's visual query still carries information about them. Inspired by the sextant's use of angular measurements to locate objects, we propose Sextant, a plug-and-play angular localization head with only about 0.035M parameters. It reuses a frozen detector, keeps track identities unchanged, and needs no extra image encoder. Sextant gives the best result in our PanoPed-S test comparison, raising the strongest baseline, MOTIP, from 47.30 to 49.49 HOTA, with gains on all eight test sequences. Without fine-tuning on real data, the same synthetic-trained heads improve MOTIP and HAT by 0.96-1.14 HOTA on real video, and both seeds improve every real sequence. HAT+Sextant scores best among the compared systems that add no localization image encoder.
作者：Qinfeng Zhu, Weiguang Zhao, Yunxi Jiang, Anh Nguyen, Lei Fan
来源
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