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QTrack

QTrack is an end-to-end vision-language model designed for query-driven multi-object tracking (MOT). Unlike traditional MOT which tracks all objects in a scene, QTrack selectively localizes and tracks specific...

Sourcehuggingface.co/GAASH-Lab/QTrack/tree/2d66b28d74d5b6aed816cb16f1986fb32c73c117

TypeOtherVersionBf16 zero pp rank 0 mp rank 00 optim statesCClasse

Source du modèle

Type: Other
Taille du fichier: 14,3 KB–48,1 GB
Version: vBf16 zero pp rank 0 mp rank 00 optim states
Mis à jour: 24 oct. 2026
Source: Hugging Face

Description de la source

QTrack: Query-Driven Reasoning for Multi-modal MOT

QTrack is an end-to-end vision-language model designed for query-driven multi-object tracking (MOT). Unlike traditional MOT which tracks all objects in a scene, QTrack selectively localizes and tracks specific targets based on natural language instructions while maintaining temporal coherence and identity consistency.

Paper: QTrack: Query-Driven Reasoning for Multi-modal MOT
Project Page: https://gaash-lab.github.io/QTrack/
Repository: https://github.com/gaash-lab/QTrack

Description

Multi-object tracking has traditionally focused on estimating trajectories of all objects. QTrack introduces a query-driven tracking paradigm that formulates tracking as a spatiotemporal reasoning problem conditioned on natural language queries.

Key Contributions

RMOT26 Benchmark: A large-scale benchmark with grounded queries and sequence-level splits to enable robust evaluation of generalization.
QTrack Model: An end-to-end vision-language model that integrates multimodal reasoning with tracking-oriented localization.
Temporal Perception-Aware Policy Optimization (TPA-PO): A structured reward strategy to encourage motion-aware reasoning.

Benchmark Results

QTrack achieves state-of-the-art performance on the RMOT26 benchmark.

Model	Params	MCP↑	MOTP↑	CLE (px)↓	NDE↓
GPT-5.2	-	0.25	0.61	94.2	0.55
Qwen3-VL-Instruct	8B	0.25	0.64	96.0	0.97
Gemma 3	27B	0.24	0.56	58.4	0.88
InternVL	8B	0.21	0.66	117.44	0.64
QTrack (Ours)	3B	0.30	0.75	44.61	0.39

Installation

To set up the environment and use the model, please follow the instructions in the official repository:

# Create conda environment
conda create -n qtrack python=3.12
conda activate qtrack

# Install QTrack and dependencies
git clone https://github.com/gaash-lab/QTrack.git
cd QTrack
pip install -r requirements.txt
pip install -e .

Citation

If you find QTrack useful for your research, please cite:

@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}
}

Sources

1 source

Hugging FacePrincipalVersion 2d66b28d74d5b6aed816cb16f1986fb32c73c117

huggingface.co/GAASH-Lab/QTrack/tree/2d66b28d74d5b6aed816cb16f1986fb32c73c117

Vérifié 24 oct.

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Extraits de sources

2 extraits

Readme6 aoûthuggingface.co/GAASH-Lab/QTrack

--- license: mit pipeline_tag: video-text-to-text tags: - multi-object-tracking - video-understanding - vision-language-model - spatiotemporal-reasoning --- # QTrack: Query-Driven Reasoning for Multi-modal MOT [**QTrack**](https://huggingface.co/papers/2603.13759) is an end-to-end vision-language model designed for query-driven multi-object tracking (MOT). Unlike traditional MOT which tracks all objects in a scene, QTrack selectively localizes and tracks specific targets based on natural language instructions while maintaining temporal coherence and identity consistency. - **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) ## Description Multi-object tracking has traditionally focused on estimating trajectories of all objects. QTrack introduces a **query-driven tracking paradigm** that formulates tracking as a spatiotemporal reasoning problem conditioned on natural language queries. ### Key Contributions - **RMOT26 Benchmark**: A large-scale benchmark with grounded queries and sequence-level splits to enable robust evaluation of generalization. - **QTrack Model**: An end-to-end vision-language model that integrates multimodal reasoning with tracking-oriented localization. - **Temporal Perception-Aware Policy Optimization (TPA-PO)**: A structured reward strategy to encourage motion-aware reasoning. ## Benchmark Results QTrack achieves state-of-the-art performance on the [RMOT26](https://huggingface.co/datasets/GAASH-Lab/RMOT26) benchmark. | Model | Params | MCP↑ | MOTP↑ | CLE (px)↓ | NDE↓ | |:-----:|:------:|:----:|:-----:|:---------:|:----:| | GPT-5.2 | - | 0.25 | 0.61 | 94.2 | 0.55 | | Qwen3-VL-Instruct | 8B | 0.25 | 0.64 | 96.0 | 0.97 | | Gemma 3 | 27B | 0.24 | 0.56 | 58.4 | 0.88 | | InternVL | 8B | 0.21 | 0.66 | 117.44 | 0.64 | | **QTrack (Ours)** | **3B** | **0.30** | **0.75** | **44.61** | **0.39** | ## Installation To set up the environment and use the model, please follow the instructions in the [official repository](https://github.com/gaash-lab/QTrack): ```bash # Create conda environment conda create -n qtrack python=3.12 conda activate qtrack # Install QTrack and dependencies git clone https://g...

Note6 aoûthuggingface.co/GAASH-Lab/QTrack

Source context: 0 downloads · 0 likes · Pipeline video-text-to-text · Repo GAASH-Lab/QTrack