Motion Detection in Football Videos

Project Overview

This project implements a motion detection system for sports videos. It identifies and highlights areas of movement using frame differencing and contour detection.

How to Run the Project

Install Dependencies

Make sure you have Python and OpenCV installed. Run:

pip install opencv-python numpy

Run the Script

This will process the video and generate two output videos in the output/ folder:

• output_raw.mp4 → Video without bounding boxes.
• output_detected.mp4 → Video with detected movement highlighted.

Approach Used

Frame Processing

• Extracts frames at 5 FPS and resizes them to 1280x720.
• Converts frames to grayscale for efficient processing.

Motion Detection (Frame Differencing)

• Computes the absolute difference between consecutive frames.
• Applies Gaussian Blur to reduce noise.
• Uses thresholding and dilation to enhance moving objects.

Contour Detection & Visualization

• Identifies motion areas using cv2.findContours().
• Filters small movements (>1200 px) to ignore noise.
• Draws bounding boxes around significant movements.

Challenges Faced

1. False Positives from Shadows & Noise
   • Used dilation and a higher contour threshold to minimize false detections.
2. Detecting Complete Players Instead of Body Parts
   • Adjusted Gaussian Blur and minimum contour area.

Author

Developed by Facundo Muruchi.