Perception

Background Subtraction

Background subtraction is a computer vision technique that segments moving or novel objects in a scene by maintaining a statistical model of the static background and flagging pixels that deviate from it. Classical methods include Gaussian mixture models per pixel and kernel density approaches, which handle gradual illumination change but struggle with camera motion and dynamic backgrounds. Deep segmentation models have largely replaced it where compute allows.

Why it matters for physical AI

Lightweight change detection still earns its keep on embedded robot platforms for workspace monitoring, safety curtains, and triggering more expensive perception only when something in the scene actually changes.