Dart 2025 Trajectory Tracking System

A computer vision-based dart trajectory tracking system for RoboMaster competitions. This project uses OpenCV to detect and track yellow darts in video footage, visualizing their flight paths in real-time or post-processing.

🎯 Features

• Real-time Dart Detection: Track yellow-colored darts using RGB color space filtering
• Trajectory Visualization: Display the complete flight path with motion trails
• Web Interface: Interactive web-based parameter tuning and visualization
• Color Range Adjustment: Built-in tools for calibrating color detection parameters
• Multi-mode Processing: Support for single dart, multi-dart, and RGB-based tracking
• Configurable Parameters: JSON-based configuration for easy parameter management

📁 Project Structure

dart_2025_trajectory/
├── src/                          # Source code
│   ├── track_dart.py            # Main tracking script
│   ├── track_dart_rgb.py        # RGB-based tracking
│   ├── track_single_dart.py     # Single dart tracking
│   ├── track_dart_web.py        # Web interface with Flask
│   └── adjust_color_range.py    # Color calibration tool
├── templates/                    # HTML templates
│   └── track_dart.html          # Web interface template
├── data/                         # Input video files
├── output/                       # Generated output videos
├── examples/                     # Example videos and results
├── dart_track_config.json       # Configuration parameters
├── requirements.txt             # Python dependencies
├── .gitignore                   # Git ignore rules
└── README.md                    # This file

🚀 Getting Started

Prerequisites

• Python 3.7+
• OpenCV
• NumPy
• Flask (for web interface)

Installation

1. Clone the repository:

git clone https://github.com/illini-robomaster/dart_2025_trajectory.git
cd dart_2025_trajectory

2. Create and activate a virtual environment (recommended):

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

3. Install dependencies:

pip install -r requirements.txt

📖 Usage

Basic Tracking

Track a dart in a video file:

python src/track_dart.py

The script will process the video and save the output with trajectory overlay.

Web Interface

Launch the interactive web interface for real-time parameter tuning:

python src/track_dart_web.py

Then open your browser and navigate to http://localhost:5000

Features:

• Real-time parameter adjustment
• Frame-by-frame analysis
• Live trajectory preview
• Configuration save/load

Color Calibration

Adjust color detection parameters for different lighting conditions:

python src/adjust_color_range.py

Use the trackbars to fine-tune HSV/RGB ranges until the dart is properly detected.

Configuration

Edit dart_track_config.json to customize tracking parameters:

{
  "R_min": 77,
  "R_max": 246,
  "G_min": 105,
  "G_max": 255,
  "B_min": 0,
  "B_max": 150,
  "Min_Area": 26,
  "Max_Area": 1906,
  "RG_ratio": 23,
  "RB_diff": 70,
  "Max_Jump": 158,
  "Exclude_Right": 480,
  "Exclude_Bottom": 701,
  "Min_Motion": 27,
  "Motion_Frames": 2
}

Key Parameters:

• R/G/B_min/max: RGB color range for dart detection
• Min/Max_Area: Size constraints for dart detection (in pixels)
• RG_ratio: Red-Green ratio threshold
• RB_diff: Red-Blue difference threshold
• Max_Jump: Maximum pixel distance between consecutive frames
• Exclude_Right/Bottom: Exclude zones to filter out false positives
• Min_Motion: Minimum motion threshold
• Motion_Frames: Number of frames to confirm motion

🎮 Scripts Overview

track_dart.py

Main tracking script with HSV-based color detection. Best for controlled lighting.

track_dart_rgb.py

RGB-based tracking with advanced filtering. Better for varied lighting conditions.

track_single_dart.py

Optimized for tracking a single dart with higher accuracy.

track_dart_web.py

Flask web application providing an interactive interface for parameter tuning and real-time visualization.

adjust_color_range.py

Interactive tool for calibrating color detection parameters using trackbars.

🛠️ Technical Details

The tracking system uses several computer vision techniques:

1. Color Space Filtering: Detects dart using RGB/HSV color thresholds
2. Contour Detection: Identifies dart shape using OpenCV contours
3. Motion Analysis: Filters false positives using motion detection
4. Trajectory Smoothing: Maintains trajectory history with deque
5. Spatial Filtering: Excludes specific regions to reduce false positives

📊 Performance Tips

• Adjust color ranges based on your lighting conditions
• Use the web interface for quick parameter iteration
• Enable exclusion zones to filter out background objects
• Tune Max_Jump to prevent tracking jumps
• Adjust Min_Area and Max_Area based on dart size in frame

🤝 Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

📝 License

This project is developed for the Illini RoboMaster team.

👥 Authors

Illini RoboMaster Team - Dart Trajectory Analysis Group

🙏 Acknowledgments

• RoboMaster Competition
• OpenCV Community
• Illini RoboMaster Team

📧 Contact

For questions or suggestions, please open an issue on GitHub.

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Note: Place your input videos in the data/ folder and output videos will be saved to output/.