Tracking Movement using OpenCV

This tutorial explains the technical workflow for using OpenCV to detect and visualize human movement through a computer camera. The process begins by downscaling video input to maximize performance before applying optical flow algorithms to generate motion vectors. By calculating the difference between live footage and flow vectors, we isolate active movement to create a mask for dynamic visual effects.

These masks are then processed through feedback loops, blur filters, and displacement nodes to produce a fluid, wavy aesthetic similar to water. Finally, the tutorial explains how to apply simplex noise and color mapping to transform the motion data into an artistic, ink-like digital display.