Physical access control apparatus driven by a mesh of surveillance sensors which distinguish abnormal from normal variance, by machine learning shared among edge computing devices

Abstract

A system transforms video frames into event metadata reports on object recognition and occupancy patterns and trends. Each camera distinguishes foreground content and uploads meta data and changes. Occupancy within bounding boxes, and object pre-cognition are transformed to non-image event data sets. Objects and persons of interest are tagged for optical tracking and correlation. Cloud analytics estimate probabilities of occupancy and change. Periodic capture is augmented by expectation of peaks and valleys. Imagery and event meta data across multiple cameras are combined for object recognition. The cloud reports and predicts regions of interest due metrics of occupancy. Each edge device is trained on the local topology of other edge devices and actuators through which objects may pass before and after entering its region of interest. Edge devices collectively initiate physical access actuator controls, predict events for other edge devices, and transmit alerts when low probability events occur.

Dependencies are inferred from the claim text.