🚗💨 Vehicle Speed Detection System

🎥 Real-time Traffic Monitoring and Speed Enforcement

🌟 What It Does

This system uses artificial intelligence to detect vehicles in video streams, track their movement, and calculate their speeds in real-time. It can:

🚙 Identify different types of vehicles (cars, trucks, buses, motorcycles) 🏎️ Measure vehicle speeds accurately 🚦 Detect and log speed limit violations 📊 Display real-time results on screen

🧩 System Components

Our Vehicle Speed Detection System is composed of several interconnected components, each playing a crucial role in the overall functionality. Here's a detailed breakdown of each component:

1. 🎬 Main Application (main.py) 🚀 Serves as the entry point for the entire system 🎭 Handles command-line arguments for video source selection 🏁 Initializes and kicks off the video processing pipeline

2. 🎞️ Video Processor (video_processor.py) 🧠 Acts as the central brain of the system 🔄 Coordinates interactions between all other components 🖥️ Manages the main processing loop for each video frame 🎚️ Implements performance optimizations like selective frame processing

3. 🚗 Vehicle Detector (detector.py) 👁️ Utilizes YOLOv8 AI model for real-time object detection 🔍 Identifies and locates vehicles in each processed frame 🏷️ Classifies detected objects into vehicle categories (e.g., car, truck, bus, motorcycle) 🎯 Applies confidence thresholding to minimize false positives

4. 🖼️ Visualizer (visualizer.py) 🎨 Draws bounding boxes around detected vehicles on the video frame 📝 Overlays vehicle information (type, speed) on the video 🚦 Color-codes speed displays based on speed limit compliance 🖥️ Manages the on-screen display of processing statistics (e.g., FPS counter)

5. 📊 Data Logger (data_logger.py) 📝 Records speed limit violations in real-time 📅 Timestamps each violation entry 📂 Saves violation data to an Excel file for later analysis 🔒 Ensures data integrity with proper file handling and error management

6. 📹 Video Capture (video_capture.py) 🎥 Handles video input from various sources (webcam, video files) 🧵 Implements multi-threading for efficient frame capture 🛠️ Provides fallback options and error handling for different video backends

7. ⏱️ Speed Tracker (speed_tracker.py) 📍 Maintains position history for each detected vehicle 🧮 Calculates vehicle speeds based on position changes over time 🔢 Implements algorithms to smooth speed calculations and reduce fluctuations

8. 🧮 Speed Calculator (speed_calculator.py) 📐 Converts pixel movements to real-world speed measurements 🧪 Applies statistical methods to filter out anomalies in speed data 🎚️ Adapts calculations based on camera perspective and positioning

9. 📏 Calibration (calibration.py) 📐 Handles camera calibration parameters 🗺️ Translates pixel coordinates to real-world distances 🔧 Provides functions to adjust for camera angle and positioning

10. ⚙️ Configuration (config.py) 🎛️ Centralizes all system-wide settings and parameters 🚥 Defines speed limits for different vehicle types 🎚️ Allows easy tuning of detection and tracking parameters 🗃️ Stores mappings for vehicle classes and their corresponding indices

🛠️ How It Works

1. Video Input: Uses your webcam or a video file
2. Vehicle Detection: Spots vehicles using YOLOv8 AI
3. Tracking: Follows each vehicle across video frames
4. Speed Calculation: Estimates speed based on vehicle movement
5. Visualization: Shows results on screen with bounding boxes and speed info
6. Logging: Records speed violations in an Excel file

📋 Requirements

Python 3.8 or newer OpenCV NumPy Ultralytics YOLOv8 openpyxl SciPy

🏃‍➡️🏃‍➡️🏃‍➡️Before Run the Program 🏃‍➡️🏃‍➡️🏃‍➡️:

Install the required packages: ( pip install opencv-python numpy ultralytics openpyxl scipy ) 📏📏OR📏📏 ( pip install -r requirements.txt )

3. Run the Program: For webcam: python main.py For video file: python main.py path/to/your/video.mp4

4. Controls: Press 'q' to quit Press 's' to save a screenshot

⚙️ Customization

Edit config.py to change: 🎯 Detection sensitivity 🏁 Speed calculation settings 🚄 Speed limits for different vehicles

📊 What You'll See

• Live video with:
• Boxes around detected vehicles
• Vehicle types (car, truck, etc.)
• Calculated speeds
• Frames per second (FPS) count
• speed_violations.xlsx file with logged violations

ScreeShot......

💡 Tips for Best Results

📏 Calibrate the camera settings in config.py for your setup 🎥 Use high-quality video input for better accuracy 💻 A computer with a good GPU will run the system faster

🔮 Future Plans

• Night-time detection improvements
• License plate recognition
• Web interface for remote monitoring
• Integration with traffic management systems

🤝 Want to Contribute?

We welcome your ideas and code contributions! Here's how:

1. Fork the repository
2. Create your feature branch
3. Commit your changes
4. Push to your branch
5. Open a Pull Request

📄 License

This project is a Open Source Project.

👥 Team

ABHISHEK SINGH Project Lead and Creater.

📞 Need Help? 🐛 Issues: GitHub Issues Page

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🌟 Created with passion by [ABHISHEK SINGH] 🌟