Improve dataset card for RMOT26: add metadata, paper, project, and code links

README.md

@@ -1,3 +1,30 @@
----
-license: mit
----
+---
+license: mit
+task_categories:
+- video-text-to-text
+---
+
+# RMOT26
+
+RMOT26 is a large-scale benchmark for **Query-Driven Multi-Object Tracking**, introduced in the paper [QTrack: Query-Driven Reasoning for Multi-modal MOT](https://huggingface.co/papers/2603.13759).
+
+- **Project Page:** [https://gaash-lab.github.io/QTrack/](https://gaash-lab.github.io/QTrack/)
+- **Repository:** [https://github.com/gaash-lab/QTrack](https://github.com/gaash-lab/QTrack)
+- **Paper:** [https://arxiv.org/abs/2603.13759](https://arxiv.org/abs/2603.13759)
+
+## Description
+
+Multi-object tracking (MOT) has traditionally focused on estimating trajectories of all objects in a video. RMOT26 introduces a query-driven tracking paradigm that formulates tracking as a spatiotemporal reasoning problem conditioned on natural language queries. 
+
+Given a reference frame, a video sequence, and a textual query, the goal is to localize and track only the target(s) specified in the query while maintaining temporal coherence and identity consistency. RMOT26 features grounded queries and sequence-level splits to prevent identity leakage and enable robust evaluation of generalization.
+
+## Citation
+
+```bibtex
+@article{ashraf2026qtrack,
+  title={QTrack: Query-Driven Reasoning for Multi-modal MOT},
+  author={Ashraf, Tajamul and Tariq, Tavaheed and Yadav, Sonia and Ul Riyaz, Abrar and Tak, Wasif and Abdar, Moloud and Bashir, Janibul},
+  journal={arXiv preprint arXiv:2603.13759},
+  year={2026}
+}
+```