# Tnl2k Eval

> Evaluates natural language-based tracking on 2000 YouTube and surveillance videos, testing the model's ability to follow and adapt to language descriptions over time. Use when the user wants to benchmark on TNL2K, or asks about evaluating this task. Reports AUC.

- Skill: `qhjqhj00/tnl2k-eval` (Agent Skill)
- Install (CLI): `npx skillmds add qhjqhj00/tnl2k-eval`
- Raw SKILL.md: https://api.skillmd.com/api/skills/qhjqhj00/tnl2k-eval/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: AI & ML
- Author: qhjqhj00 (https://skillmd.com/u/qhjqhj00)
- Updated: 2026-09-08
- Page: https://skillmd.com/skills/qhjqhj00/tnl2k-eval

---


# tnl2k-eval

> ReasoningTrack: Chain-of-Thought Reasoning for Long-term Vision-Language Tracking — Xiao Wang et al. (2025) (arXiv:2508.05221, 2025)

## What this evaluates

Evaluates natural language-based tracking on 2000 YouTube and surveillance videos, testing the model's ability to follow and adapt to language descriptions over time.

## Datasets

- **TNL2K** — total 2000; splits: train (1300), test (700)

## Metrics

- `AUC` **(primary)** — range: percent
  - Area Under the Curve: area under the precision plot across varying distance thresholds.
- `PR` — range: percent
  - Precision Rate: percentage of frames where the center distance is within 20 pixels.

## Input / output format

**Input**: Video frames, initial language description, and ground-truth bounding boxes.

**Output**: Predicted bounding box coordinates per frame.

## Scoring recipe

```python
For each frame, compute center distance d.
PR = (count(d < 20) / N) * 100
AUC = integral of precision plot over thresholds [0, 50]
```

## Common pitfalls

- YouTube videos often contain rapid motion and occlusions that challenge language grounding.
- Baseline methods may not be fine-tuned on TNL2K, making direct comparison sensitive to training data.

## Evidence (verbatim from paper)

> The dataset is divided into 1300 training videos and 700 test videos. As shown in Table[VI], our method achieves competitive performance, improving by 3% in PR over the second-ranked method.

## Citation

```bibtex
@misc{wang2025reasoningtrack,
  title={ReasoningTrack: Chain-of-Thought Reasoning for Long-term Vision-Language Tracking},
  author={Xiao Wang et al. (2025)},
  year={2025},
  note={arXiv:2508.05221}
}
```

- arXiv: 2508.05221