SBIR-STTR Award

This Small Business Innovation Research (SBIR) Phase I project will constitute design of algorithms, toolsets and interfaces to detect human activity from video and improve operator’s wide area situational awareness of live feeds from multiple aerial and ground sensors in the battlefield. The key innovations in the proposed effort are: 1) automated and robust detection of atomic actions that may span multiple sensors, 2) extraction and exploitation of scene context for improving activity discovery, 3) an interactive activity specification framework for effective threat specification and automatic recognition, tuned towards the requirements of the operator in the battlefield, 4) automatic context-enabled wide area activity recognition by using information from multiple heterogeneous sensors and 5) enhanced user interface based on NASA’s WorldWind geo-browser plug-in for wide area situational awareness of the operator. In addition, metrics for activity detection evaluation will be investigated and a thorough performance analysis of the proposed innovation will carried out. to ensure robustness and This will ensure that a scalable and robust baseline activity discovery system is developed by the end of Phase I.

Keywords:
Wide Area Automated Activity Discovery, Scene Context Exploitation, Interactive Activity Specification Framework (Ask), Site Wide Threat Analysis, Nasa Worldwind Geo-Browser,

The Phase I effort of the SBIR program culminated in the development of algorithms, toolsets and interfaces to detect human activity from video and improve the operator’s wide area situational awareness of live feeds from multiple video sensors in the battlefield. In Phase II, we plan to extend this technology and achieve the following objectives: 1) utilization of scene context extraction algorithms, in conjunction with scene context exploitation algorithms for improved activity detection; 2) detection of suspicious events throughout the site of interest (regardless of if they are visible in the same camera view); 3) target-centric queries as opposed to area-centric queries (tripwire, area of interest breach) as well as several other UI enhancements; 4) view and camera invariant target matching for non-overlapping cross-camera target re-acquisition and tracking; 5) warning of threats before they actually materialize; 6) automated best shot analysis of the detected targets in the scene; 7) integration with distributed data sharing environments such as GHub; and 8) improved human activity discovery using novel features. In addition, metrics for activity detection evaluation will be investigated and a thorough performance analysis of the proposed innovations will be carried out.

Keywords:
Human Activity Discovery, Scene Context Exploitation, Situational Awareness, Target-Centric Queries, Cross-Camera Tracking, Best Shot Analysis, Ghub, Ask Framework.