This project demonstrates a basic motion detection system using OpenCV, a powerful computer vision library Techniques Used
A popular computer vision library used to process images and videos.
We use a static background image and compare it to each frame of the video to detect changes or motion.
Converts color images to grayscale to simplify processing by reducing the amount of data.
Applies a blur effect to the images to reduce noise and improve the accuracy of motion detection.
Computes the difference between the background and the current frame to highlight areas of change.
Creates a binary image where significant changes are highlighted, making it easier to detect motion.
Finds and outlines the shapes of moving objects based on the thresholded image.
Marks detected moving objects with rectangles for visualization.
Understanding how to convert images to grayscale and apply blurring to prepare for motion detection. Motion Detection Basics:
Learning how to compare images to detect changes and isolate moving objects. Contour and Shape Detection:
Gaining skills in finding and drawing contours to highlight objects of interest. Video Processing:
Handling video frames and applying image processing techniques in real-time.
This project showcases the fundamental techniques of motion detection and provides a practical understanding of how to use OpenCV for real-time video analysis. By combining grayscale conversion, blurring, thresholding, and contour detection, you can identify and highlight moving objects effectively.