# PanoPed: Beyond Bounding Boxes for Sim-to-Real Panoramic Pedestrian Tracking

arXiv: https://arxiv.org/abs/2610.08826 · HTML: https://mlsanity.com/paper/2610.08826/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 29 of its 36 references that are in the map.
- First posted on arXiv 25 Sep 2026.

## Builds on (most cited first, 29 of 29)

- [End-to-End Object Detection with Transformers](https://mlsanity.com/paper/2005.12872/) (2020)
- [Deformable DETR: Deformable Transformers for End-to-End Object Detection](https://mlsanity.com/paper/2010.04159/) (2020)
- [SAM 2: Segment Anything in Images and Videos](https://mlsanity.com/paper/2408.00714/) (2024)
- [ByteTrack: Multi-Object Tracking by Associating Every Detection Box](https://mlsanity.com/paper/2110.06864/) (2021)
- [HOTA: A Higher Order Metric for Evaluating Multi-Object Tracking](https://mlsanity.com/paper/2009.07736/) (2020)
- [MOT16: A Benchmark for Multi-Object Tracking](https://mlsanity.com/paper/1603.00831/) (2016)
- [TrackFormer: Multi-Object Tracking with Transformers](https://mlsanity.com/paper/2101.02702/) (2021)
- [MOTR: End-to-End Multiple-Object Tracking with Transformer](https://mlsanity.com/paper/2105.03247/) (2021)
- [Segment Anything in High Quality](https://mlsanity.com/paper/2306.01567/) (2023)
- [Observation-Centric SORT: Rethinking SORT for Robust Multi-Object Tracking](https://mlsanity.com/paper/2203.14360/) (2022)
- [DanceTrack: Multi-Object Tracking in Uniform Appearance and Diverse Motion](https://mlsanity.com/paper/2111.14690/) (2021)
- [MOTChallenge: A Benchmark for Single-Camera Multiple Target Tracking](https://mlsanity.com/paper/2010.07548/) (2020)
- [Learning to Detect and Track Visible and Occluded Body Joints in a Virtual World](https://mlsanity.com/paper/1803.08319/) (2018)
- [D-FINE: Redefine Regression Task in DETRs as Fine-grained Distribution Refinement](https://mlsanity.com/paper/2410.13842/) (2024)
- [MOTSynth: How Can Synthetic Data Help Pedestrian Detection and Tracking?](https://mlsanity.com/paper/2108.09518/) (2021)
- [Multiple Object Tracking as ID Prediction](https://mlsanity.com/paper/2403.16848/) (2024)
- [Matching Anything by Segmenting Anything](https://mlsanity.com/paper/2406.04221/) (2024)
- [360-Indoor: Towards Learning Real-World Objects in 360deg Indoor Equirectangular Images](https://mlsanity.com/paper/1910.01712/) (2019)
- [Omnidirectional Multi-Object Tracking](https://mlsanity.com/paper/2503.04565/) (2025)
- [360VOT: A New Benchmark Dataset for Omnidirectional Visual Object Tracking](https://mlsanity.com/paper/2307.14630/) (2023)
- [JRDB-PanoTrack: An Open-world Panoptic Segmentation and Tracking Robotic Dataset in Crowded Human Environments](https://mlsanity.com/paper/2404.01686/) (2024)
- [Unbiased IoU for Spherical Image Object Detection](https://mlsanity.com/paper/2108.08029/) (2021)
- [OmniTrack++: Omnidirectional Multi-Object Tracking by Learning Large-FoV Trajectory Feedback](https://mlsanity.com/paper/2511.00510/) (2025)
- [SPEAR: A Simulator for Photorealistic Embodied AI Research](https://mlsanity.com/paper/2607.06701/) (2026)
- [Sekai2: From World Exploration to Interactive World Modeling](https://mlsanity.com/paper/2608.09449/) (2026)
- [History-Aware Transformation of ReID Features for Multiple Object Tracking](https://mlsanity.com/paper/2503.12562/) (2025)
- [SO3UFormer: Learning Intrinsic Spherical Features for Rotation-Robust Panoramic Dense Prediction](https://mlsanity.com/paper/2602.22867/) (2026)
- [WorldRover: A Scalable Synthetic Video Data Engine for World Exploration with Rich Annotations](https://mlsanity.com/paper/2608.15659/) (2026)
- [Panoramic Scene Understanding: A Survey from Distortion-Aware Engineering to Sphere-Native Modeling](https://mlsanity.com/paper/2606.27745/) (2026)

## Abstract (arXiv)

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.