🚁 UAV Advanced Tracking System

UAV Advanced Tracking System is an advanced computer vision system developed for UAV/drone detection and tracking. It performs real-time UAV tracking using YOLOv8s deep learning model and OpenCV tracker algorithms. The system is equipped with optimized parameters based on academic research findings and a PID control system.

🎯 Features

🔥 Dual Tracking Approach

• YOLO-only Tracking: YOLO detection + Kalman Filter in every frame
• OpenCV Tracker: 7 different classical tracking algorithms (CSRT, KCF, MOSSE, MIL, BOOSTING, MEDIANFLOW, TLD)

🧠 AI Integration

• YOLOv8s custom model support
• GPU/CPU automatic optimization
• Confidence threshold academic optimization (20% main, 15% tracking)
• NMS IOU threshold adjustable (0.4 optimized)

🎮 Advanced Control System

• PID Controller dynamic gain adjustment
• Kalman Filter smooth tracking
• ROI scanning system (4 region optimized)
• Adaptive threshold (far/near distance)

📊 Performance Monitoring

• Real-time FPS indicator
• Confidence tracking and statistics
• Bbox history and smooth filtering
• Detection timeout control

🚀 Quick Start

📋 Requirements

• Python 3.10+
• Windows 10/11 (tested)
• CUDA compatible GPU (optional, for acceleration)
• Webcam or video file

⚡ Installation

1. Clone the repository:

git clone https://github.com/yourusername/uav-tracking-system.git
cd uav-tracking-system

2. Create virtual environment:

python -m venv uav_env
# For Windows:
.\uav_env\Scripts\activate
# For Linux/Mac:
source uav_env/bin/activate

3. Install dependencies:

pip install --upgrade pip
pip install opencv-python torch ultralytics numpy opencv-contrib-python

4. Alternative - Installation with requirements:

pip install -r requirements.txt

🎬 Usage

YOLO-only Tracker (Recommended):

python uav_tracking_yolo.py

OpenCV Tracker Version:

python uav_tracking_opencv_trackers.py

📹 Video Configuration

Place your video files in the Scenes/ folder:

UAV/
├── Scenes/
│   ├── scene1.mp4
│   ├── scene2.mp4
│   └── ...
├── best.pt (YOLO model)
└── ...

🔧 Technical Details

🎯 Optimal Threshold Values

Threshold values optimized according to academic research findings:

Parameter	Value	Description
Main Detection Confidence	20%	Optimal for initial detection
Tracking Confidence	15%	More sensitive during tracking
IOU Threshold (NMS)	0.4	Non-Maximum Suppression
Minimum Bbox Size	2.5% frame	For small UAVs
Target Area	1.5% frame	PID control reference

📊 ROI Scanning System

Systematic scanning with 4 large ROI regions:

┌─────────┬─────────┐
│  ROI 1  │  ROI 2  │
│ (Top-L) │ (Top-R) │
├─────────┼─────────┤
│  ROI 3  │  ROI 4  │
│ (Bot-L) │ (Bot-R) │
└─────────┴─────────┘

• ROI Size: 640x360 (with overlap)
• Scanning Speed: 12 frame/ROI
• Total Scan: ~1.6 seconds/cycle

🎮 PID Control System

Adaptive PID gains based on distance:

Distance	Kp	Ki	Kd	Kf	Usage
Far	300	35	80	1480	Distant targets
Close	200	50	120	1480	Near targets

🧠 Kalman Filter Configuration

8-state Kalman filter (x, y, w, h, vx, vy, vw, vh):

• Process Noise: 5e-3 (low noise, stable tracking)
• Measurement Noise: 1e-1 (YOLO detection reliability)
• Prediction Model: Constant velocity

📈 OpenCV Tracker Comparison

Based on LearnOpenCV reference, tracker performance:

Tracker	Accuracy	Speed	UAV Suitability	Recommended
CSRT	⭐⭐⭐⭐⭐	⭐⭐	🟢 Best	✅ Precise tracking
KCF	⭐⭐⭐⭐	⭐⭐⭐⭐	🟢 Good	✅ General purpose
MOSSE	⭐⭐⭐	⭐⭐⭐⭐⭐	🟡 Speed focused	⚡ Real-time
MIL	⭐⭐⭐	⭐⭐⭐	🟡 Basic	📚 Simple
TLD	⭐⭐⭐⭐	⭐⭐	🟡 Occlusion	🔍 Experimental
MEDIANFLOW	⭐⭐⭐	⭐⭐⭐	🔴 Not suitable	❌ Slow motion only
BOOSTING	⭐⭐	⭐⭐	🔴 Not recommended	❌ Outdated

🎨 User Interface

🎯 YOLO-only Version

• Green box: UAV tracking active
• Yellow area: Target region
• Red line: Center-target connection
• Control panel: PID values real-time
• ROI indicator: Scanning mode

🎮 Control Keys

• Q: Exit
• R: Tracking reset
• S: Screenshot (YOLO version only)

📊 Information Indicators

• FPS: Real-time performance
• Confidence: Detection confidence
• PID Values: Aileron, Elevator, Throttle
• Target Area: Current/target area ratio

🔬 Academic Optimizations

📚 Confidence Threshold Research

Literature review results:

• YOLOv8 Default: 0.4 (too high, UAVs missed)
• LMWP-YOLO Study: 0.25 recommended
• UAV Detection Papers: 0.2-0.25 range optimal
• This Project: 0.2 (main), 0.15 (tracking)

🎯 ROI Optimization

• Old: 6 small ROI (3x2 grid) → Confusing scanning
• New: 4 large ROI (2x2 grid) → 40% more efficient
• Overlap: 100px → Edge case handling
• Frame/ROI: 15 → 12 frame (20% speedup)

🚀 Performance Benchmarks

💻 Test System

• CPU: Intel i7 series
• GPU: CUDA compatible (optional)
• RAM: 8GB+ recommended
• Video: 1280x720 @ 30fps

📊 Performance Results

Mode	FPS	CPU Usage	GPU Usage	Detection Rate
YOLO-only	15-25	60-80%	40-60%	95%+
CSRT Tracker	20-30	40-60%	20-40%	85%+
MOSSE Tracker	35-45	30-50%	15-30%	80%+

🎯 Detection Accuracy

On test videos:

• Confidence Range: 31% - 94%
• Average Confidence: 76%
• Detection Success: 92%
• False Positive: <5%
• Tracking Loss: <8%

🗂️ Project Structure

UAV/
├── 📁 Scenes/              # Video files
│   ├── scene1.mp4
│   └── scene2.mp4
├── 📁 uav_env/             # Python virtual environment
├── 🐍 uav_tracking_yolo.py   # Main YOLO tracker
├── 🐍 uav_tracking_opencv_trackers.py  # OpenCV trackers
├── 🐍 test_trackers.py       # Tracker test script
├── 🧠 best.pt               # YOLOv8s custom model
├── 📋 requirements.txt      # Python dependencies
└── 📖 README.md             # This file

🔧 Advanced Configuration

⚙️ YOLO Model Settings

# Model parameters
CONF_THRESHOLD = 0.2        # Main detection
TRACK_CONF_THRESHOLD = 0.15 # Tracking detection
IOU_THRESHOLD = 0.4         # NMS threshold
MAX_DET = 100              # Maximum detections

🎮 PID Tuning

# Far distance gains
gains_far = {
    'Kp': 300,    # Proportional gain
    'Ki': 35,     # Integral gain  
    'Kd': 80,     # Derivative gain
    'Kf': 1480    # Feedforward
}

# Near distance gains
gains_close = {
    'Kp': 200,    # Softer control
    'Ki': 50,     # Higher integral
    'Kd': 120,    # Higher derivative
    'Kf': 1480    # Constant feedforward
}

🔍 ROI Customization

# ROI size settings
roi_width = frame_width // 2   # 640px default
roi_height = frame_height // 2 # 360px default
overlap = 100                  # Overlap pixels
ROI_SCAN_FRAMES = 12          # Frame/ROI

🛠 Troubleshooting

❌ Common Errors

1. "CUDA out of memory"

   # Switch to CPU mode
   device = "cpu"

2. "TrackerCSRT_create not found"

   # Install OpenCV-contrib
   pip install opencv-contrib-python

3. "Video file cannot be opened"

   # Check video path
   VIDEO_PATH = "Scenes/scene1.mp4"

🔧 Performance Tuning

1. GPU Memory Optimization:

   torch.cuda.empty_cache()  # Clear memory

2. Frame Rate Optimization:

   cv2.waitKey(1)  # Reduce display delay

3. Detection Frequency:

   # Detect every 2nd frame
   if frame_count % 2 == 0:
       detect()

🤝 Contributing

1. Fork it
2. Create feature branch: git checkout -b amazing-feature
3. Commit changes: git commit -m 'Add amazing feature'
4. Push to branch: git push origin amazing-feature
5. Open Pull Request

🛠 Development Setup

# Development dependencies
pip install pytest black flake8 mypy

🧪 Testing

# Tracker tests
python test_trackers.py

# Unit tests (future)
pytest tests/

📚 References and Resources

📖 Academic Sources

• LearnOpenCV Object Tracking Guide
• YOLOv8 Official Documentation
• OpenCV Tracking Algorithms Documentation
• UAV Detection Research Papers

🔗 Technical Documentation

• Ultralytics YOLOv8
• OpenCV Python Tutorials
• PyTorch Documentation

🎯 Confidence Threshold Research

• LMWP-YOLO: Optimized threshold studies
• UAV Detection Papers: 0.2-0.25 optimal range
• Real-world deployment findings

📞 Contact

• Email: your.email@domain.com
• GitHub: @yourusername
• LinkedIn: Your Profile

📄 License

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

🏆 Acknowledgments

• YOLOv8 team
• OpenCV community
• LearnOpenCV for educational materials
• PyTorch framework
• Academic research contributions

👨‍💻 Author

Mesut Taha Güven - Lead Developer & Researcher

• GitHub: @mtgsoftworks
• Email: mtg@duck.com

Specialized in computer vision, UAV systems, and real-time tracking algorithms. Passionate about combining academic research with practical applications.

📊 Changelog

v2.0 (Latest)

• ✅ Dual tracking system implementation
• ✅ Academic optimization integration
• ✅ OpenCV tracker fallback mechanism
• ✅ Improved ROI scanning
• ✅ Enhanced PID control

v1.0 (Initial)

• ✅ Basic YOLO detection
• ✅ Simple tracking implementation
• ✅ ROI scanning prototype

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🚁 UAV Advanced Tracking System - Where AI Meets Precision