The Problem:

During snowfall or heavy rain, the AI Person/Vehicle detection identifies static landscape objects (garden posts, parked equipment, fences) as humans or vehicles. While these objects are ignored in clear weather, the visual "noise" from precipitation causes the AI to misidentify them.

Currently, "Activity Zones" are not a solution because they create permanent blind spots in the security perimeter.

Proposed Functionality:

I am requesting a "Mark as False Positive" or "Ignore this Object" feature.

AI Feedback Loop: When a user receives a false alert (e.g., a garden post seen as a person), allow the user to long-press the event and select "This is a static object, ignore in future."

Static Object Calibration: Allow users to "tag" known static objects in the field of view (like a tractor or a post) so the AI filters those specific coordinates/shapes out of its alert logic while remaining active for new movement in that same area.

Environmental Adaptation: This would allow the AI to maintain high sensitivity for real threats while ignoring the "ghost" patterns created by snow interacting with static objects.

This would move Tapo from simple shape-matching to a more robust, environment-aware security system.