This project implements real-time object detection and tracking for vehicles using YOLOv3 and Kalman filtering. It provides functionalities like:

• Vehicle Detection: Employs YOLOv3 to identify vehicles (class ID 2) within video frames.
• Kalman Filter Tracking: Predicts vehicle positions across frames for smoother tracking and velocity estimation.
• Hungarian Algorithm: Efficiently associates detections with tracked objects.
• Speed Calculation: Estimates vehicle speed in meters per second based on calibrated pixel-to-meter conversion.

Features:

• Accurate object detection using YOLOv3.
• Smooth and robust vehicle tracking with Kalman filtering.
• Efficient association of detections with tracked objects.
• Real-time performance for video stream processing.

Requirements:

• Python 3.x
• OpenCV (cv2)
• NumPy (np)
• SciPy (scipy) (for linear_sum_assignment)
• Matplotlib (optional, for visualization)

Installation:

• Ensure you have Python 3.x installed.
• Install the required libraries using pip: pip install opencv-python numpy scipy matplotlib

Usage:

• Place your pre-trained YOLOv3 weights (yolov3.weights), configuration (yolov3.cfg), and class names file (coco.names) in the project directory.
• Update the pixel_to_meter function in the code with your calibrated value based on camera settings.
• Replace ‘video.mp4’ in the cap = cv2.VideoCapture(‘video.mp4’) line with the path to your video file.
• Run the script: python rapid_recognition.py

Disclaimer:

This project is provided for educational and research purposes. Vehicle detection and tracking accuracy may vary depending on video quality, lighting conditions, and scene complexity. Calibrate the pixel_to_meter conversion for reliable speed estimation.

License:

This project is licensed under the MIT License.

Further Enhancements:

• Explore alternative object detection models for improved accuracy or efficiency.
• Implement multi-object tracking for handling multiple vehicles simultaneously.
• Integrate with display libraries for real-time visualization of bounding boxes and speed information.
• Consider incorporating deep learning-based speed estimation methods for potentially higher accuracy.

Contributing:

We welcome contributions to this project! Feel free to submit pull requests with enhancements, bug fixes, or improvements to the code or documentation.

Contact:

Please feel free to reach out for any questions or feedback.

Additional Notes:

• Feel free to adjust the README.md content to better reflect your specific project goals and features. Consider adding examples, visualizations, or code snippets for improved clarity.
• Ensure the installation instructions are accurate and up-to-date with the required libraries.