Create README.md

README.md

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+---
+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://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:
+
+```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}
+}
+```