Tnllt Eval

tnllt-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 long-term vision-language tracking capability by measuring localization accuracy over extended video sequences while dynamically updating natural language descriptions to handle appearance changes and occlusions.

Datasets

• TNLLT — total 200; splits: test (-1)

Metrics

• PR (primary) — range: percent
  • Precision Rate: percentage of frames where the center distance between predicted and ground-truth boxes is within 20 pixels.
• NPR — range: percent
  • Normalized Precision Rate: percentage of frames where the center distance is within a dynamic threshold scaled to the ground-truth object size.
• SR — range: percent
  • Success Rate: percentage of frames where the Intersection-over-Union (IoU) between predicted and ground-truth boxes exceeds 0.5.

Input / output format

Input: Video frames (search and template), initial language description, and ground-truth bounding boxes.

Output: Updated language description and predicted bounding box coordinates per frame.

Scoring recipe

For each frame, compute center distance d between predicted and GT box.
PR = (count(d < 20) / N) * 100
NPR = (count(d < threshold) / N) * 100
SR = (count(IoU(pred, GT) > 0.5) / N) * 100

Common pitfalls

• Text update interval significantly impacts accuracy; optimal interval is dataset-dependent (e.g., 100 frames on TNLLT).
• Cascaded text updates can suffer from error accumulation, making initial text sometimes more robust than refined text.
• Static text integration can improve robustness compared to purely dynamic updates.

Evidence (verbatim from paper)

The dataset is evaluated using three metrics: precision (P), normalize precision(NP) and success rate(SR) metrics. As shown in Table[III], our method has achieved the best results among the various approaches in recent years, with an PR of 74.1%, NPR of 77.0%, SR of 63.9%.

Citation

@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