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

Data collection method:

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 ensuring high labeling accuracy. The labeling process included object detection, tracking, and identity association across multiple cameras.

Data processing and preparation activities:

No third-party datasets are used.

Technical details and requirements:

The data was collected using fixed-position traffic surveillance cameras provided by an industrial partner. Each camera recorded 4K-resolution video at 15 frames per second under real-world traffic conditions. The annotation process was conducted using a custom-built semi-automated labeling toolkit developed in Python, running on Ubuntu 20.04. Key libraries and frameworks used include OpenCV, NumPy, and Matplotlib for visualization and annotation support. To view and utilize the dataset, users will require basic tools for handling image and text data (e.g., Python with OpenCV) and a machine with sufficient storage and memory to process high-resolution video frames and annotation files. The dataset follows YOLO-style text annotations for detection tasks and CSV-format files for tracking metadata. For reproducibility, we provide the labeling toolkit and evaluation scripts in the associated GitHub repository, along with documentation detailing the annotation format and dataset structure.

Methodology link: