Geo-trax: A Comprehensive Framework for Georeferenced Vehicle Trajectory Extraction from Drone Imagery

Geo-trax (GEO-referenced TRAjectory eXtraction) is a comprehensive pipeline that extracts high-accuracy, georeferenced vehicle trajectories from high-altitude drone imagery. Built for quasi-stationary aerial monitoring of urban traffic, it turns raw bird's-eye view (BEV) drone footage into precise, real-world vehicle trajectories. The framework combines YOLO detection, multi-object tracking, and video stabilization with a robust orthophoto-based georeferencing stage, producing GNSS-tagged, lane-resolved trajectories that are spatially and temporally consistent and ready for large-scale traffic analysis and simulation. It is optimized for urban intersections and arterial corridors, where high-fidelity, vehicle-level insights drive intelligent transportation systems and digital twin applications.

📌 Important: If you use this code in your work, kindly acknowledge it by citing the following article:

Robert Fonod, Haechan Cho, Hwasoo Yeo, Nikolas Geroliminis (2025). Advanced computer vision for extracting georeferenced vehicle trajectories from drone imagery, Transportation Research Part C: Emerging Technologies, vol. 178, 105205. DOI: 10.1016/j.trc.2025.105205

Why Geo-trax

• 🛰️ Real-world output: georeferenced, lane-resolved trajectories (WGS84 + local CRS) with per-vehicle speed, acceleration, and estimated dimensions, straight from raw BEV drone video.
• 🎯 Accurate detection: YOLOv8s vehicle detector reaching 0.951 mAP@50, trained on more than 19,000 annotated aerial images.
• 🚗 Flexible tracking: four vehicle classes and six selectable multi-object trackers (BoT-SORT, ByteTrack, OC-SORT, and more).
• 🌀 Drone-motion robust: homography-based stabilization (Stabilo) plus orthophoto image registration for consistent, cross-flight coordinates.
• 📊 Proven at scale: powered the Songdo Traffic dataset (roughly 700,000 trajectories across 20 intersections, fleet of 10 drones; see Real-World Deployment below).
• ⚙️ One command, one config: geotrax batch runs the whole pipeline; a single YAML drives every stage, with four tuned presets included.

Features

• Detection: YOLOv8s on aerial BEV imagery; detects car (incl. vans), bus, truck, and motorcycle.
• Tracking: six multi-object trackers (BoT-SORT default); see Tracking for a comparison; optional per-track frame-gap interpolation.
• Stabilization: homography-based trajectory correction via Stabilo 🌀, tuned with Stabilo-Optimize 🎯.
• Georeferencing: frame-to-orthophoto registration; outputs lat/lon, local CRS, speed, acceleration, and lane assignment per vehicle.
• Visualization: track overlays on original, stabilized, or static-reference video, in five rendering modes (incl. oriented bounding boxes).
• Analysis: trajectory maps, kinematic distributions, and class/dimension charts, per-video or aggregated across drones and sessions.
• Scaling & tooling: batch-processes directory trees and aggregates multi-drone data; includes standalone utilities for end-to-end data preparation, training, evaluation, and validation.

🚀 Planned Enhancements

• Comprehensive documentation in a dedicated docs/ folder. A tools/README.md index already covers the auxiliary scripts.
• Modularized, OOP-based pipeline with custom reference frame support and georeferencing leveraging Stabilo's image-matching backend.
• Per-class confidence thresholds.
• SAHI-based small-object detection.
• Batch inference, GPU-accelerated image registration, and multi-thread processing.
• Real-world map visualization (e.g., MovingPandas, contextily) and interactive web app.

🔗 Related Projects

Geo-trax integrates with and complements several specialized tools:

• Stabilo 🌀: Python library for video and trajectory stabilization using robust homography transformations. Supports various feature detectors, RANSAC algorithms, and user-defined masks. Used as Geo-trax's core stabilization engine.
• Stabilo-Optimize 🎯: benchmarking and hyperparameter optimization framework for Stabilo. Evaluates stabilization performance through ground truth-free assessment using random perturbations. Used to fine-tune Geo-trax stabilization parameters.
• HBB2OBB 📦: converts horizontal bounding boxes to oriented bounding boxes using SAM segmentation models. Can enhance Geo-trax outputs when object orientation is needed for downstream analysis.

Real-World Deployment: The Songdo Experiment

Geo-trax was validated in a large-scale urban traffic monitoring campaign in Songdo, South Korea, where it processed footage from a fleet of 10 drones to produce the Songdo Traffic dataset. The detection model was trained on the companion Songdo Vision dataset. Both are described in the associated publication.

🎥 Demo of Geo-trax applied to the Songdo experiment: https://youtu.be/gOGivL9FFLk

Citation

If you use Geo-trax in your research or software, please cite:

1. Journal article (preferred for any use of the framework):

   @article{fonod2025advanced,
     title = {Advanced computer vision for extracting georeferenced vehicle trajectories from drone imagery},
     author = {Fonod, Robert and Cho, Haechan and Yeo, Hwasoo and Geroliminis, Nikolas},
     journal = {Transportation Research Part C: Emerging Technologies},
     volume = {178},
     pages = {105205},
     year = {2025},
     publisher = {Elsevier},
     doi = {10.1016/j.trc.2025.105205},
     url = {https://doi.org/10.1016/j.trc.2025.105205}
   }

2. Software archive (when referencing or building on the code itself):

   @software{fonod2026geo-trax,
     author = {Fonod, Robert},
     title = {Geo-trax: A Comprehensive Framework for Georeferenced Vehicle Trajectory Extraction from Drone Imagery},
     year = {2026},
     month = jul,
     version = {1.1.0},
     doi = {10.5281/zenodo.12119542},
     url = {https://github.com/rfonod/geo-trax},
     license = {MIT}
   }

Contributions

Early code received key contributions from Haechan Cho (georeferencing) and Sohyeong Kim (video/flight-log merging). Community contributions are welcome: open a GitHub Issue or submit a pull request.

License

This project is distributed under the MIT License. See the LICENSE file for more details.

Full Changelog

https://github.com/rfonod/geo-trax/compare/v1.0.1...v1.1.0