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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2603.13759 |
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| _version_ | 1866911515097956352 |
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| author | Ashraf, Tajamul Tariq, Tavaheed Yadav, Sonia Riyaz, Abrar Ul Tak, Wasif Abdar, Moloud Bashir, Janibul |
| author_facet | Ashraf, Tajamul Tariq, Tavaheed Yadav, Sonia Riyaz, Abrar Ul Tak, Wasif Abdar, Moloud Bashir, Janibul |
| contents | Multi-object tracking (MOT) has traditionally focused on estimating trajectories of all objects in a video, without selectively reasoning about user-specified targets under semantic instructions. In this work, we introduce 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. To support this setting, we construct RMOT26, a large-scale benchmark with grounded queries and sequence-level splits to prevent identity leakage and enable robust evaluation of generalization. We further present QTrack, an end-to-end vision-language model that integrates multimodal reasoning with tracking-oriented localization. Additionally, we introduce a Temporal Perception-Aware Policy Optimization strategy with structured rewards to encourage motion-aware reasoning. Extensive experiments demonstrate the effectiveness of our approach for reasoning-centric, language-guided tracking. Code and data are available at https://github.com/gaash-lab/QTrack |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_13759 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | QTrack: Query-Driven Reasoning for Multi-modal MOT Ashraf, Tajamul Tariq, Tavaheed Yadav, Sonia Riyaz, Abrar Ul Tak, Wasif Abdar, Moloud Bashir, Janibul Computer Vision and Pattern Recognition Multi-object tracking (MOT) has traditionally focused on estimating trajectories of all objects in a video, without selectively reasoning about user-specified targets under semantic instructions. In this work, we introduce 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. To support this setting, we construct RMOT26, a large-scale benchmark with grounded queries and sequence-level splits to prevent identity leakage and enable robust evaluation of generalization. We further present QTrack, an end-to-end vision-language model that integrates multimodal reasoning with tracking-oriented localization. Additionally, we introduce a Temporal Perception-Aware Policy Optimization strategy with structured rewards to encourage motion-aware reasoning. Extensive experiments demonstrate the effectiveness of our approach for reasoning-centric, language-guided tracking. Code and data are available at https://github.com/gaash-lab/QTrack |
| title | QTrack: Query-Driven Reasoning for Multi-modal MOT |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2603.13759 |