The first version of my auto-tracking camera shook like it was nervous.
Here's the trap. You tell a camera to keep a person centered, so the obvious rule is: if they're not dead-center, move toward them. But "dead-center" is never exactly true — there's always a pixel or two of error — so the servos chase noise forever and the whole rig vibrates.
If you could teach the camera the difference between off and close enough, it would stop fighting itself. And if the correction scaled with how far off the target actually was — a gentle nudge when they're nearly centered, a fast sweep when they're at the edge — motion would look smooth instead of snapping and overshooting.
That's the whole fix, and it's exactly how I built Netra, an edge-AI camera that follows a person on its own: proportional control for the speed, and a 12% dead zone in the middle where the servos simply do nothing. Two rules from control theory did more for tracking quality than any amount of model tuning.
The lesson generalizes past cameras: a system that reacts to every tiny error is a system that never settles. Sometimes the smartest thing your code can do is ignore small differences on purpose.
More things I've built and written about → www.divyakush.com
