For base and entry security, the Army has a system to collect faces and identify them in large photo and video collections.  Cybernet will extend this capability to incorporate vehicles, persons, and weapons recognition into this capability so that they can be correlated and identified as friend of foe (i.e. non-threat and threat).

Cybernet built for the Army Research Office (and later the Air Force and the Joint IED Organization) a learning-based computer vision system that incorporates (1) hand or manual and machine learning capabilities, (2) truth data collection and processing, (c) location and history retention, and (d) animals and threat recognition learning and automatic tagging.  This system has been applied to generic recognition and tracking of vehicles, person & carried items, generically potential threats, and species of animal for environment reporting and identification counts.

Our system includes tools and Machine Learning (ML) methodology (and existing vehicle, person, and carried item recognition codes) that should readily port to the Army environment for an immediate proof of concept result in this Small Business Innovative Research effort, and can be extended through processing and assessment of larger training sets to continuously improve recognition rates and threat-Identify pairing performance.