metadata
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.
- Project Page: https://gaash-lab.github.io/QTrack/
- Repository: https://github.com/gaash-lab/QTrack
- Paper: 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
@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}
}