RoundaboutHD: High-Resolution Real-World Urban Environment Benchmark for Multi-Camera Vehicle Tracking

TL;DR

RoundaboutHD is a high-resolution, real-world multi-camera vehicle tracking benchmark dataset capturing complex roundabout scenarios, designed to advance research in vehicle tracking, detection, and re-identification in urban environments.

Contribution

It introduces a comprehensive, high-resolution dataset with diverse, real-world roundabout scenarios, including annotations, vehicle models, and camera geometry, filling a gap in existing datasets.

Findings

Baseline results for detection, tracking, and re-identification provided.

Dataset includes 40 minutes of high-res video with 512 vehicle identities.

Supports multiple tasks: detection, single-camera tracking, ReID, and multi-camera tracking.

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