jye9/Multi-Camera-Multi-Vehicle-Tracking-System

Multi-Camera Multi-Vehicle Tracking System (UAV Dataset)

This dataset contains synchronized multi-UAV video footage and comprehensive tracking annotations, accompanying the paper A Topology-Aware Spatiotemporal Handover Framework for Continuous Multi-UAV Tracking.

It is designed for evaluating real-time multi-camera multi-vehicle tracking (MCMT) systems, focusing on solving trajectory fragmentation and maintaining global identity persistence across isolated UAV fields of view.

Dataset Contents

1. Video Assets

Three synchronized 1-minute video clips captured from different UAV perspectives in complex urban environments.

• uav1_1min.mp4
• uav2_1min.mp4
• uav3_1min.mp4

Example: Consistent global tracking (e.g., the red car with ID 70) across overlapping UAV fields of view.

2. Annotations (CSVs)

The dataset includes structured CSV files with rich metadata generated by our tracking pipeline.

Base Schema:

frame_index, x_min, y_min, x_max, y_max, class_id, confidence, tracker_id, global_id, camera_id, speed, status

Note: Speed is estimated in km/h via pixel-to-meter scaling. Status tracks active/new_entry/exiting states.

Tracking Pipeline Code

The full production-ready pipeline (utilizing YOLO11, ByteTrack, and our Topology-based Spatiotemporal Handover mechanism) used to process these videos is open-source.

🔗 View Source Code on GitHub

Citation

If you find this dataset or our framework useful in your research, please cite our paper:

@misc{ye2026topologyaware,
      title={A Topology-Aware Spatiotemporal Handover Framework for Continuous Multi-UAV Tracking}, 
      author={Jianlin Ye and Christos Kyrkou and Panayiotis Kolios},
      year={2026},
      eprint={2605.15779},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}