RoundaboutHD: High-Resolution Real-World Urban Environment Benchmark for Multi-Camera Vehicle Tracking
Journal:
arXiv
Published Date:
Jul 11, 2025
Abstract
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 and the evaluation code are publicly available at:
https://github.com/siri-rouser/RoundaboutHD.git
