---
license: apache-2.0
tags:
  - multi-object-tracking
  - DETR
  - computer-vision
  - CVPR2026
datasets:
  - DanceTrack
  - SportsMOT
  - BFT
language:
  - en
---

# FDTA: From Detection to Association

[![arXiv](https://img.shields.io/badge/ArXiv-2512.02392-B31B1B.svg)](https://arxiv.org/abs/2512.02392)
[![GitHub](https://img.shields.io/badge/GitHub-FDTA-blue?logo=github)](https://github.com/Spongebobbbbbbbb/FDTA)

Official model weights for the paper **"From Detection to Association: Learning Discriminative Object Embeddings for Multi-Object Tracking"** (CVPR 2026).

![image](https://cdn-uploads.huggingface.co/production/uploads/663063e9ce65a66ed8d90aff/jY6efxZCU3eWPWXi8Mp65.png)

> **TL;DR.** We reveal that DETR-based end-to-end MOT suffers from overly similar object embeddings. FDTA explicitly enhances discriminativeness in this paradigm.


## Available Checkpoints

| File | Dataset | 
|------|---------|
| `dancetrack.pth` | DanceTrack |
| `sportsmot.pth` | SportsMOT | 

## Main Results
**DanceTrack**

| Training Data | HOTA | IDF1 | AssA | MOTA | DetA |
|---------------|------|------|------|------|------|
| train         | 71.7 | 77.2 | 63.5 | 91.3 | 81.0 |
| train+val     | 74.4 | 80.0 | 67.0 | 92.2 | 82.7 |

**SportsMOT**
| Training Data | HOTA | IDF1 | AssA | MOTA | DetA |
|---------------|------|------|------|------|------|
| train         | 74.2 | 78.5 | 65.5 | 93.0 | 84.1 |

**BFT**
| Training Data | HOTA | IDF1 | AssA | MOTA | DetA |
|---------------|------|------|------|------|------|
| train         | 72.2 | 84.2 | 74.5 | 78.2 | 70.1 |

## Usage
**Download Checkpoints**
```python
from huggingface_hub import hf_hub_download

# Download the DanceTrack checkpoint
ckpt_path = hf_hub_download(
    repo_id="Spongebobbbbbbbb/FDTA",
    filename="dancetrack.pth",
    local_dir="./checkpoints/"
)
```
For full training and evaluation instructions, please refer to the [GitHub repository](https://github.com/Spongebobbbbbbbb/FDTA).

## Citation
```bibtex
@article{shao2025fdta,
  title={From Detection to Association: Learning Discriminative Object Embeddings for Multi-Object Tracking},
  author={Shao, Yuqing and Yang, Yuchen and Yu, Rui and Li, Weilong and Guo, Xu and Yan, Huaicheng and Wang, Wei and Sun, Xiao},
  journal={arXiv preprint arXiv:2512.02392},
  year={2025}
}
```

## License
This project is released under the [MIT License](https://opensource.org/licenses/MIT).