Optical Flow Ball

This demo/project explores interactive motion tracking using optical flow.
It demonstrates two implementations — one in Python and one in C++ to compare approaches across languages and APIs.

Purpose

• apply optical flow methods (Lucas-Kanade method).
• Build an interactive demo where a virtual ball reacts to motion in a live feed.
• Compare the ergonomics of Python (fast prototyping) vs. C++ (fine-grained control, performance).

Key Outcomes

• Python version: Feature tracking with trails; proof of concept.
• C++ version: Localized ROI interaction; ball responds only to motion within its region.
• Cross-language learning: Demonstrates equivalent logic in two ecosystems.

Structure

optical_flow_ball/
    python/ #python implementation
    cpp/ #c++ implementation
    README.MD