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README.md

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+# RoundaboutHD:High-Resolution Real-World Urban Environment Benchmark for Multi-Camera Vehicle Tracking
+
+[![Paper](http://img.shields.io/badge/paper-arXiv%3A2507.08729-B31B1B.svg)](https://arxiv.org/abs/2507.08729)
+
+**News(21/07/2025):**
+
+ We have submitted this paper to [WACV 2026](https://wacv.thecvf.com/).
+ 
+ ---
+
+**RoundaboutHD** is a comprehensive, high-resolution multi-camera vehicle tracking (MCVT) dataset captured in a real-world roundabout scenario. It is designed to support the development and benchmarking of object detection, single-camera tracking (SCT), and multi-camera vehicle tracking (MCVT) algorithms in urban environments with nonlinear motion and frequent occlusions.
+
+<p align="center">
+    <img src="layout.jpg" alt="RoundaboutHD Example" width="400"/>
+</p>
+
+
+## Dataset Access
+
+The full content of RoundaboutHD is under RoundaboutHD.zip file.
+
+---
+
+
+## Dataset Description
+
+RoundaboutHD provides **40 minutes of fully annotated video footage** recorded from **4 non-overlapping 4K cameras at 15 FPS**. Each camera covers 10 minutes of traffic under various conditions: normal, light, and heavy traffic. The roundabout layout introduces significant challenges such as:
+
+- **Nonlinear vehicle trajectories**
+- **Frequent occlusions** due to infrastructure (e.g., a central statue)
+- **Multiple exits and intersections** between cameras
+
+This makes RoundaboutHD a challenging yet realistic benchmark for evaluating vehicle tracking performance in smart city applications.
+
+---
+
+## Dataset Contents
+
+The dataset structure:
+```
+RoundaboutHD/
+├── imagesc001/  ← Same structure applies to imagesc002, imagesc003, imagesc004
+│   ├── detection/
+│   │   ├── labels_GT.zip/
+│   │   │   ├── img000000.txt
+│   │   │   ├── ...
+│   │   │   └── img008999.txt  # Total: 9000 files
+│   │   ├── labels_test.zip/
+│   │   └── labels_xy.zip/
+│   ├── SCT/
+│   │   └── imagesc001_SCT_GT.txt
+│   ├── geo-mapping/
+│   │   ├── cam01_fitted_cam.json
+│   │   ├── cam01_info.png
+│   │   ├── cam01_topview.jpg
+│   │   ├── cam01_trace.png
+│   │   └── cam01_undistorted.png
+│   └── video.mp4
+├── imagesc002/
+├── imagesc003/
+├── imagesc004/
+├── ReID_subset/
+│   └── ReID_subset.zip/
+│       ├── query_images/
+│       ├── test_images/
+│       └── train_images/
+├── Multi_CAM_Ground_Truth.txt
+└── vehicle_statistic.xlsx
+```
+In this dataset, it contains:
+- **Labeled video footage** 
+- **Object detection Ground Turth**
+- **Single-camera tracking (SCT) Ground Turth**
+- **camera modelling parameter and visualization**
+- **RoundaboutHD image-based ReID subset**
+- **Image-based ReID Ground Turth**
+- **Multi-camera tracking (MCVT) Ground Turth**
+- **Vehicle context information**
+- **Evaluation scripts and label format documentation**
+---
+
+## Evaluation
+
+We provide tools for evaluating tracking performance in this repository:  
+[Multi-Camera Tracking Labelling Tool](https://github.com/siri-rouser/multi_camera_tracking_labelling_tool.git)
+
+### Multi-Camera Tracking Evaluation
+
+Use the following command:
+
+```bash
+python eval_label.py <prediction_file> <ground_truth_file>
+```
+
+Each line in the prediction/ground-truth file should follow this format:
+
+```
+<camera_id> <obj_id> <frame_id> <xmin> <ymin> <width> <height> <xworld> <yworld>
+```
+
+**Descriptions:**
+
+- `camera_id`: Integer identifier (1–4 in RoundaboutHD)
+- `obj_id`: Object ID (positive integer, consistent across cameras)
+- `frame_id`: Frame number (starting at 0)
+- `xmin`, `ymin`, `width`, `height`: Bounding box coordinates (pixels)
+- `xworld`, `yworld`: GPS/world coordinates of the object (optional)
+
+> *Note: The value of `xworld` and `yworld` do not count into the evaluation, you can use the value -1 as placehold.
+---
+
+### Single-Camera Tracking Evaluation
+
+Use the following command:
+
+```bash
+python eval_det.py <prediction_file> <ground_truth_file>
+```
+Each line in the prediction/ground-truth file should follow this format:
+```
+<frame_id> <obj_id> <xmin> <ymin> <xmax> <ymax>
+```
+
+**Descriptions:**
+The definition of each item is as same as the multi-camera tracking format.
+
+### Object Detection Evaluation
+Use the following command:
+
+```bash
+python eval_sct.py <prediction_directory> <ground_truth_directory>
+```
+Each directory should contain multiple `.txt` files named by frame with format f'img{frame_id:06d}.txt', example as below:
+
+```
+img000000.txt, img000001.txt, ...
+```
+
+Each file must contain object detections in the format:
+
+```
+<class_id> <xmin> <ymin> <xmax> <ymax>
+```
+**Descriptions:**
+`class_id` corresponds to the category names pre-defined in the COCO dataset.
+> *Note: The value of `class_id` do not influence the results for object detection evaluation.
+
+---
+## Baseline
+
+We evaluate our dataset using **ELECTRICITY** [1], a general and reproducible MCVT method. We set the **distance threshold** to **12** and apply a **hard removal distance** of **80**. Static vehicle trajectories are removed. The result is shown below:
+
+| Dataset           | IDF1  | IDP   | IDR   |
+|-------------------|-------|-------|-------|
+| **RoundaboutHD**  | 28.14 | 26.45 | 30.06 |
+
+---
+
+## Citation
+
+If you use **RoundaboutHD** in your research, please cite:
+
+```
+@misc{lin2025roundabouthdhighresolutionrealworldurban,
+      title={RoundaboutHD: High-Resolution Real-World Urban Environment Benchmark for Multi-Camera Vehicle Tracking}, 
+      author={Yuqiang Lin and Sam Lockyer and Mingxuan Sui and Li Gan and Florian Stanek and Markus Zarbock and Wenbin Li and Adrian Evans and Nic Zhang},
+      year={2025},
+      eprint={2507.08729},
+      archivePrefix={arXiv},
+      primaryClass={cs.CV},
+      url={https://arxiv.org/abs/2507.08729}, 
+}
+```
+
+---
+
+[1] Y. Qian et al., "ELECTRICITY: An Efficient Multi-Camera Vehicle Tracking System for Intelligent City", CVPRW, 2020.