Tracking the Motion of a Ball Using Image Moments and Centroid Analysis 🎥⚽

Tracking the Motion of a Ball Using Image Moments and Centroid Analysis – Project Description

Overview:
This project focuses on tracking a small moving object (e.g., a ball) across video frames using image moments and centroid analysis. The approach calculates the object’s center of mass in each frame, reconstructs its trajectory, and computes motion metrics such as step distances, total distance, net displacement, and average speed. The project demonstrates fundamental concepts in computer vision, image processing, and motion analysis using Python and OpenCV.

Suitable For:

• BS Computer Science / Software Engineering – practical computer vision and image processing applications

• BS Mathematics / Applied Mathematics – applied coordinate geometry and vector calculations

• BS Physics – motion analysis and kinematics

Technologies Used:

• Programming Language: Python

• Libraries & Tools: OpenCV, NumPy, Matplotlib, CSV

• Techniques: Image moments, centroid calculation, step distance computation, trajectory visualization

• Visualization: Trajectory plots, annotated frames, and optional pixel-to-centimeter scaling

Features:

• Detection of a small colored object in each video frame

• Centroid-based tracking of the object’s position over time

• Step-by-step computation of step distances, total distance, net displacement, and average speed

• Visualization of trajectory using plotted centroids and connecting lines

• Optionally convert pixel distances to real-world units (cm)

• Fully documented Python scripts suitable for experimentation and learning

Deliverables:

• Complete Python source code with step-by-step explanations

• Sample input video and/or constructed frames for demonstration

• Output video with object trajectory overlay and temporary PNG frames

• CSV file of frame-wise centroid coordinates and step distances

• Project documentation and report template in LaTeX with figures

Benefits for Students:

• Hands-on experience with computer vision techniques for object tracking

• Learn to implement image processing pipelines using Python and OpenCV

• Understand motion metrics calculation and trajectory visualization

• Gain skills in coding, data visualization, and mathematical modeling

• Easily demoable for academic presentations or portfolio projects

Highlights of Novelty:

• Simple yet effective centroid-based tracking for small objects

• Iterative computation of image moments for precise centroid calculation

• Step distances, total distance, net displacement, and average speed computed and visualized

• Integration of computer vision concepts with basic kinematics for practical learning