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README.md

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-license: apache-2.0
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+---
+license: apache-2.0
+---
+
+# QuadTrack
+
+<!-- Provide a quick summary of the dataset. -->
+
+QuadTrack is a dataset designed for multi-object tracking (MOT) research, with a focus on panoramic and long-span scenarios. It provides challenging tracking sequences that include drastic appearance variations, prolonged occlusions, and wide field-of-view distortions, enabling the development and evaluation of robust MOT algorithms.
+
+## Dataset Details
+
+### Dataset Description
+
+<!-- Provide a longer summary of what this dataset is. -->
+
+
+
+- **Curated by:** [HNU CVPU]
+- **Funded by [optional]:** [National Natural Science Foundation of China (No.62473139 and No.12174341), Zhejiang Provincial Natural Science Foundation of China (Grant No. LZ24F050003) and Shanghai SUPREMIND Technology Co. Ltd.]
+- **Shared by [optional]:** [HNU CVPU]
+- **License:** [CC BY-NC 4.0]
+
+### Dataset Sources [optional]
+
+<!-- Provide the basic links for the dataset. -->
+
+- **Repository:** [https://github.com/xifen523/OmniTrack]
+- **Paper:** [https://arxiv.org/abs/2503.04565]
+- **Demo:** [https://www.youtube.com/watch?v=Q3mvzBtkkeU]
+
+## Uses
+
+<!-- Address questions around how the dataset is intended to be used. -->
+
+### Direct Use
+
+<!-- This section describes suitable use cases for the dataset. -->
+
+QuadTrack is designed for multi-object tracking (MOT) research, particularly in panoramic.
+
+
+## Dataset Structure
+
+<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
+The dataset is organized into two main splits: train and test.
+```bash
+QuadTrack/
+├── train/                # Training set
+│   ├── img1/             # Training images (video frames)
+│   └── gt/               # Ground-truth annotations (bounding boxes, IDs, etc.)
+│
+└── test/                 # Test set
+    └── img1/             # Test images (no ground-truth provided)
+```
+
+
+
+## Dataset Creation
+
+### Curation Rationale
+
+<!-- Motivation for the creation of this dataset. -->
+
+QuadTrack was created to address the limitations of existing multi-object tracking (MOT) datasets, which often focus on narrow field-of-view scenarios and short-term associations. In contrast, panoramic and long-span tracking poses unique challenges such as:
+
++ Prolonged occlusions leading to identity switches.
+
++ Wide field-of-view distortions caused by panoramic cameras.
+
++ Dramatic appearance variations across long sequences.
+
+The dataset aims to provide a benchmark for developing algorithms that achieve long-term identity stability and robust re-identification in real-world panoramic environments.
+
+### Source Data
+
+<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
+
+#### Data Collection and Processing
+
+<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->
+
+Collection: The video sequences were captured using panoramic and wide-angle cameras in complex real-world environments (e.g., urban traffic, crowded public areas).
+
++ Annotation:
+
+  + Bounding boxes and unique object IDs were assigned frame-by-frame.
+
+  + Annotations follow the standard MOTChallenge format for compatibility.
+
++ Processing:
+
+  + Frames were extracted at fixed intervals to balance temporal resolution and storage.
+
+  + Quality checks ensured consistency in ID assignment across long occlusions.
+
+  + Tools used: https://www.cvat.ai/
+#### Who are the source data producers?
+
+<!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. -->
+
+The source videos were collected and annotated by the QuadTrack research team.
+
++ Producers: Internal annotation team trained for MOT labeling tasks.
+
++ Demographics: Not applicable, as the dataset focuses on object trajectories rather than personal or sensitive identity information.
+
++ Note: No personally identifiable information (PII) is included. The dataset is curated strictly for research purposes.
+
+
+## Bias, Risks, and Limitations
+
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+
+While QuadTrack provides challenging panoramic multi-object tracking scenarios, several limitations and risks should be noted:
+
++ Domain bias: The dataset primarily consists of panoramic and wide field-of-view sequences. Models trained on QuadTrack may not generalize well to conventional narrow-angle tracking datasets.
+
++ Scene diversity: Although collected across different environments, the dataset may not cover all possible real-world scenarios (e.g., extreme weather, night-time, or thermal imagery).
+
++ Annotation errors: Despite quality control, occasional inaccuracies in bounding boxes or identity switches may exist, especially under heavy occlusion.
+
++ Ethical risks: As a vision dataset, improper use in surveillance or privacy-intrusive applications could raise ethical concerns.
+
+### Recommendations
+
+<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+
+Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
+
+## Citation [optional]
+
+<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
+
+**BibTeX:**
+```bibtex
+@inproceedings{luo2025omnidirectional,
+  title={Omnidirectional Multi-Object Tracking},
+  author={Luo, Kai and Shi, Hao and Wu, Sheng and Teng, Fei and Duan, Mengfei and Huang, Chang and Wang, Yuhang and Wang, Kaiwei and Yang, Kailun},
+  booktitle={Proceedings of the Computer Vision and Pattern Recognition Conference},
+  pages={21959--21969},
+  year={2025}
+}
+```
+
+**APA:**
+
+```mathematica
+Luo, K., Shi, H., Wu, S., Teng, F., Duan, M., Huang, C., Wang, Y., Wang, K., & Yang, K. (2025). Omnidirectional multi-object tracking. *Proceedings of the Computer Vision and Pattern Recognition Conference*, 21959–21969.
+
+```
+
+
+
+## Dataset Card Authors [optional]
+
+xifen527
+
+## Dataset Card Contact
+
+kailun.yang@hnu.edu.cn, luokai@hnu.edu.cn