Hj-Lee/The-DRIFT

The DRIFT Open Dataset

Overview

DRIFT (Drone-derived Intelligent for Traffic analysis) is an open-source dataset designed to support advanced traffic behavior research using high-resolution drone imagery. It enables accurate vehicle detection, trajectory tracking, and traffic flow analysis across complex urban intersections.

Objectives

• Provide a large-scale, annotated drone dataset optimized for traffic analysis
• Support urban mobility research with pre-trained models and analytical tools
• Enable multi-scale traffic analysis (microscopic, mesoscopic, and macroscopic)

Contributions

• o detect and track vehicle instances in high resolution using polygon-based oriented bounding boxes (OBB) at an altitude of 250 meters
• To stabilize video data and real-world orthophoto-mapped trajectories
• To provide 81,699 annotated vehicle trajectories collected across 2.6 km of urban roadways
• To offer object detection/tracking model for customization and the built-in tools for lane-change analysis, time-to-collision (TTC), congestion detection, flow-density analysis, and more

Dataset Specifications![ttc_new]

• Site coverage: 9 interconnected urban intersections in Daejeon, South Korea
  • Target site: From 99 Daehak-ro to 291 Daehak-ro Daejeon in South Korea
• Imagery: 4K drone footage with frame-level annotations
• Trajectory format: Real-world coordinates with speed, acceleration, and heading
• Model: YOLOv11m + ByteTrack with polygon-based OBB detection

Demos

• Frame stabilization
• Result of object detection using YOLOv11m model (approximately 300K vehicle instances manually annotated)
• Visualized trajectories in DRIFT open dataset

Quick Start

# Clone the repository
git clone https://github.com/AIxMobility/The-DRIFT

Load Dataset

# DRIFT dataset load
from datasets import load_dataset

dataset = load_dataset("Hj-Lee/The-DRIFT")

Model Customization

# Create conda env
conda create -n DRIFT python=3.11 -y

# Install dependencies
pip install -r requirements.txt

# Preprocess the dataset
sh extraction/preprocessing/run.sh

# Stabilize drone video
sh extraction/stabilo/run.sh

# Train detection model
python model/train.py

Repository Structure

│
├── data/                      # Raw and processed drone data
│   ├── csv/                   # Frame-level trajectory metadata
│   ├── sample_video/          # Sample drone videos
│   ├── site_images/           # Reference frames in each site
│
├── extraction/                # Data extraction and stabilization
│   ├── preprocessing/         # Preprocessing pipeline
│   │   ├── detect_and_track.py
│   │   ├── json_to_csv.py             
│   │   ├── lane.py
│   │   ├── RoI.json
│   │   ├── run.sh
│   ├── stabilo/               # Stabilization scripts (Ack.: https://github.com/rfonod/stabilo)
│   │   ├── run.sh
|
├── model/                     # Annotation data and model training
│   ├── test/                   
│   ├── train/           
│   ├── valid/
│   ├── data.yaml                   
│   ├── drone_data.yaml           
│   ├── train.py
│   ├── best.pt
|
├── utils/                     # Utility scripts for data handling
│   ├── convert.py
│   ├── video_to_frame.py
│
├── vis/                       # Visualization scripts and tools
│
├── notebooks/
│   ├── data_exploration.ipynb
│   ├── performance_analysis.ipynb
│
├── requirements.txt
├── README.md
└── LICENSE

Visualizations of Traffic Analysis Tools

Scale	Description	Image
Microscopic	Lane Change (LC)
	Time-to-Collision (TTC)
Mesoscopic	Flow-Density Diagram
	Time-Space Diagram
Macroscopic	Speed Heatmap

Citation

If you use this project in your academic research, commercial products, or any published material, please acknowledge its use by citing it.

@misc{lee2025driftopendatasetdronederived,
      title={DRIFT open dataset: A drone-derived intelligence for traffic analysis in urban environment}, 
      author={Hyejin Lee and Seokjun Hong and Jeonghoon Song and Haechan Cho and Zhixiong Jin and Byeonghun Kim and Joobin Jin and Jaegyun Im and Byeongjoon Noh and Hwasoo Yeo},
      year={2025},
      eprint={2504.11019},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2504.11019}, 
}