---
license: cc-by-nc-4.0
task_categories:
  - image-feature-extraction
  - other
  
language:
  - en
  
pretty_name: FEOT

tags:
- Event-Camera
- Object-Tracking

size_categories:
- 10K<n<100K
---

# FEOT Dataset
## Overview
FEOT is a high spatial resolution Frame-Event tracking dataset with pixel-level alignment and occlusion-level annotations, serving as a reliable benchmark for evaluating occlusion-robust single-object trackers. 
Feel free to download and use！ The dataset homepage is https://feot-dataset.github.io/.

---

## Features
- Frame-event dual-mode (Prophesee EVK4 + FLIR BFS-U3-32S4M)
- Pixel-level alignment (Co-axial Optical System)
- High spatial resolution (1070 x 610)
- Occlusion level annotation (11 levels)
---

## Dataset Structure

```text
FEOT root folder/
├──Sequence_000001/
   ├── event
      ├──000001.png
      ├──000002.png
      ├──...
   ├── frame
      ├──000001.png
      ├──000002.png
      ├──...
   ├── aligned_event.h5
   ├── attribute.txt
   ├── gt.txt
   └── timestamp.txt
├──Sequence_000002/
├──Sequence_000003/
...
```
---

## Supplementary Notes
- **Note on Dataset Sequence Count.** The total number of sequences released in the current version (370 sequences) is slightly higher than the number reported in the original paper. This is primarily due to the inclusion of additional sequences that were rigorously filtered and added during the final stage of data curation and quality verification, aiming to enhance scene diversity and cover a broader range of occlusion patterns.
- **Privacy Protection Measures.** To strictly safeguard any potential personal privacy, we have applied mosaic blurring to all regions containing human faces in the dataset. This processing step does not compromise the validity of the occlusion tracking annotations, while ensuring compliance with data privacy and ethical standards. The results reported in the paper are based on data before mosaicking.
---

## Citation
```bibtex
@InProceedings{Dong2026CVPR,
        title = {Tracking through Severe Occlusion via Event-Derived Transient Cues},
        author = {Hao Dong, Yujin Liu, Haoyue Liu, Zhenyu Wang, Shihan Peng, Zhiwei Shi, Yi Chang, Luxin Yan},
        booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
        pages={XXX--XXX},
        year = {2026}
}
```