Docs
Concepts

Target lock

The target-lock design combines a current track, an appearance template, and predicted motion so a selected object can be followed across short gaps.

This page describes intended runtime behavior. There is no public target-lock service or field-performance guarantee attached to the current website.

Zones express “tell me about anything that happens here.” Target lock expresses the other primitive operators actually use: “this one. Follow this one.” Between them they cover most of aerial overwatch.

What a lock is#

A lock binds three things to the clicked object: its current track, an appearance template sampled at lock time, and a motion model. The lock manager runs them as a voting system. While the detector and tracker keep producing the object, the lock simply rides the track. When they don't, the template and motion model carry it.

Localized re-detection#

The lock also changes how detection itself runs. Each frame, the runtime crops a region around the lock's predicted position, upscales it, and runs a dedicated detection pass on the crop. An object too small or too dim for the full-frame pass is usually obvious in the upscaled crop. This is what makes clicking a person at night work: the full frame shows a smear, the crop shows a person.

Locks and zones are designed to compose. A vehicle lock can produce an area-entry event, and the event can refer to a retained path when that artifact exists.

Losing honestly#

Every lock carries a confidence that decays when the object goes unobserved and recovers when it's re-confirmed. Below threshold, the lock declares itself lost and freezes at its last confident position instead of wandering onto whatever nearby object resembles the template. Reacquisition then works outward from that position. As with zones, the rule is that the system tells you it lost the target; it never quietly substitutes a different one.

Multiple locks#

The design allows several locks, each with its own crop, template, and confidence. Actual capacity depends on the model, input resolution, hardware, and latency target.