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.