Datasets:

xifen527
/

QuadTrack

@@ -1,48 +1,44 @@
 ---
-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/
@@ -54,108 +50,57 @@ QuadTrack/
     └── 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

 ---
+license: cc-by-nc-4.0
+task_categories:
+- object-detection
+tags:
+- multi-object-tracking
+- panoramic-vision
+- robotics
 ---
 # QuadTrack
 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.
+It was introduced in the paper [Omnidirectional Multi-Object Tracking](https://huggingface.co/papers/2503.04565) (CVPR 2025) and is part of the EmboTrack benchmark featured in [OmniTrack++: Omnidirectional Multi-Object Tracking by Learning Large-FoV Trajectory Feedback](https://huggingface.co/papers/2511.00510).
 ## Dataset Details
 ### Dataset Description
+- **Curated by:** HNU CVPU
+- **Funded by:** 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:** HNU CVPU
+- **License:** CC BY-NC 4.0
+### Dataset Sources
+- **Repository:** [https://github.com/xifen523/OmniTrack](https://github.com/xifen523/OmniTrack)
+- **Papers:**
+  - [OmniTrack (CVPR 2025)](https://huggingface.co/papers/2503.04565)
+  - [OmniTrack++](https://huggingface.co/papers/2511.00510)
+- **Demo:** [https://www.youtube.com/watch?v=Q3mvzBtkkeU](https://www.youtube.com/watch?v=Q3mvzBtkkeU)
 ## Uses
 ### Direct Use
+QuadTrack is designed for multi-object tracking (MOT) research, particularly in panoramic and wide field-of-view scenarios.
 ## Dataset Structure
 The dataset is organized into two main splits: train and test.
 ```bash
 QuadTrack/
     └── img1/             # Test images (no ground-truth provided)
 ```
 ## Dataset Creation
 ### Curation Rationale
 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
 #### Data Collection and Processing
+- **Collection:** The video sequences were captured using panoramic and wide-angle cameras in complex real-world environments (e.g., urban traffic, crowded public areas) using platforms like quadruped robots.
+- **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:** [CVAT](https://www.cvat.ai/)
 #### Who are the source data producers?
+The source videos were collected and annotated by the internal research team at HNU CVPU. No personally identifiable information (PII) is included; the dataset is curated strictly for research purposes.
 ## Bias, Risks, and Limitations
+- **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 or night-time).
+- **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.
+## Citation
 **BibTeX:**
 ```bibtex
+@inproceedings{luo2025omniTrack,
   title={Omnidirectional Multi-Object Tracking},
+  author={Kai Luo, Hao Shi, Sheng Wu, Fei Teng, Mengfei Duan, Chang Huang, Yuhang Wang, Kaiwei Wang, Kailun Yang},
+  booktitle={IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
   year={2025}
 }
+@article{luo2025omnitrackplus,
+  title={OmniTrack++: Omnidirectional Multi-Object Tracking by Learning Large-FoV Trajectory Feedback},
+  author={Kai Luo and others},
+  journal={arXiv preprint arXiv:2511.00510},
+  year={2025}
+}
 ```
 ## Dataset Card Contact
 kailun.yang@hnu.edu.cn, luokai@hnu.edu.cn