| 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} | |
| } | |
| ``` |