In this project, PhysicsAI is developing a spatiotemporal video perception capability based on end-to-end deep neural networks (DNNs) for exploiting full-motion EO/IR sensors mounted on vehicles to provide a real-time on-the-move threat detection capability. Spatiotemporal deep learning provides fully-integrated spatial and temporal machine perception of robust target features that evolve in real-time from vehicle-borne video cameras and is capable of producing multiple types of outputs such as object classification, localization, and tracking.
In Phase II, PhysicsAI will develop a prototype mounted on ground and aerial vehicles to demonstrate real-time on-the-move threat detection in representative mission scenarios. Applications for this technology include intelligent sensors and autonomous vehicles.
SBIR Topic A18-041 is sponsored by the U.S. Army Combat Capabilities Development Command C5ISR.