At a local-level, each sensor builds a background model, which we diff against & combine w/ inference outputs for detections (background modeling helps reduce our false-positive rate). At a global level, we continuously push new pre-trained models over-the-air. These are built using 3rd party data sources (so not sourced from the sensors themselves).