| Title: |
Dataset for, 'RoundaboutHD: High-Resolution Real-World Urban Environment Benchmark for Multi-Camera Vehicle Tracking' |
| Authors: |
Lin, Yuqiang; Lockyer, Sam |
| Contributors: |
Sui, Michael; Gan, Lee; Stanek, Florian; Zarbock, Markus; Li, Wenbin; Evans, Adrian; Zhang, Nic |
| Publisher Information: |
University of Bath |
| Publication Year: |
2025 |
| Collection: |
University of Bath Research Data Archive |
| Subject Terms: |
Transportation Operations and Management; Artificial Intelligence Technologies; Image and Vision Computing; Multimedia |
| Description: |
The multi-camera vehicle tracking (MCVT) framework holds significant potential for smart city applications, including anomaly detection, traffic density estimation, and suspect vehicle tracking. However, current publicly available datasets exhibit limitations, such as overly simplistic scenarios, low-resolution footage, and insufficiently diverse conditions, creating a considerable gap between academic research and real-world scenario. To fill this gap, we introduce RoundaboutHD, a comprehensive, high-resolution multi-camera vehicle tracking benchmark dataset specifically designed to represent real-world roundabout scenarios. RoundaboutHD provides a total of 40 minutes of labelled video footage captured by four non-overlapping, high-resolution (4K resolution, 15 fps) cameras. In total, 512 unique vehicle identities are annotated across different camera views, offering rich cross-camera association data. RoundaboutHD offers temporal consistency video footage and enhanced challenges, including increased occlusions and nonlinear movement inside the roundabout. In addition to the full MCVT dataset, several subsets are also available for object detection, single camera tracking, and image-based vehicle re-identification (ReID) tasks. Vehicle model information and camera modelling/ geometry information are also included to support further analysis. We provide baseline results for vehicle detection, single-camera tracking, image-based vehicle re-identification, and multi-camera tracking. The dataset is publicly available. ; The dataset was collected using fixed-position, real-world traffic cameras located in Indiana, USA, provided by an industrial partner under a collaborative agreement. The video footage was captured under natural driving conditions, without experimental interference, to reflect realistic urban traffic patterns. All annotations were manually curated using a custom-built semi-automated labeling toolkit developed specifically for this project. This tool significantly enhanced annotation efficiency while ... |
| Document Type: |
dataset |
| File Description: |
other |
| Language: |
English |
| Relation: |
https://researchdata.bath.ac.uk/1574/1/RoundaboutHD.zip; Lin, Y., Lockyer, S., Starwit Technologies GmbH, 2025. Dataset for, "RoundaboutHD: High-Resolution Real-World Urban Environment Benchmark for Multi-Camera Vehicle Tracking". Bath: University of Bath Research Data Archive. Available from: https://doi.org/10.15125/BATH-01574 . |
| Availability: |
https://researchdata.bath.ac.uk/1574/; https://researchdata.bath.ac.uk/1574/1/RoundaboutHD.zip |
| Rights: |
cc_mit |
| Accession Number: |
edsbas.B8FD36B9 |
| Database: |
BASE |