Motion recording captures video only when the system detects change in the scene, and stops when the scene is still. The aim is to spend storage on the moments that matter and save it during the long stretches when nothing moves — an empty corridor at 3 am produces no recording. It is the classic storage-saving mode, controlled by a motion trigger (pixel change or, better, AI object detection) plus detection regions and sensitivity.
The savings can be large on low-activity cameras: a view that is quiet most of the day records a fraction of what continuous mode would, cutting storage and making the archive easier to review (you scrub through events, not hours of stillness). A crucial detail is the pre-event buffer — the system continuously holds a few seconds in memory and prepends them when a trigger fires, so the recording includes the run-up to the event rather than starting after it has begun.
The pitfalls are blind spots and trigger quality. If motion is mis-tuned or a trigger fails, the event is simply not recorded — there is no footage to recover, unlike continuous mode. Pixel-based motion also drowns in false triggers outdoors (trees, rain, light), which either wastes storage or, if desensitised to compensate, misses real events; AI object triggers cut false starts by 80–95% and make motion recording far more trustworthy. Use motion recording where activity is genuinely sparse, always enable a pre-event buffer, and prefer object-based triggers over raw pixel motion.
