Human behavior digitization and intent recognition using data modeling

Holger M. Jaenisch; James W. Handley; Kristina L. Jaenisch; Nathaniel G. Albritton

doi:10.1117/12.817769

30 April 2009 Human behavior digitization and intent recognition using data modeling

Holger M. Jaenisch, James W. Handley, Kristina L. Jaenisch, Nathaniel G. Albritton

Proceedings Volume 7346, Visual Analytics for Homeland Defense and Security; 73460E (2009) https://doi.org/10.1117/12.817769
Event: SPIE Defense, Security, and Sensing, 2009, Orlando, Florida, United States

Abstract

Autonomous and network centric smart cameras for use in homeland security and other human activities monitoring applications require a multi-layer approach for real time image processing. We propose a novel method to achieve behavior digitization and preemptive course of action (COA) analysis by converting temporal and spatial pixel subframes into a form that can be encoded into equation based Data Models. Output from these Data Models is fused with evidence and sensor data in the COA decision cascade, which recommends COAs that yield evidence. Evidence from the decision cascade continues to be amassed until the hypothesized threat forms a strong enough conviction to initiate alert responses and external intercepting events. This paper outlines our proposed methodology and approach.

Citation Download Citation

Holger M. Jaenisch, James W. Handley, Kristina L. Jaenisch, and Nathaniel G. Albritton "Human behavior digitization and intent recognition using data modeling", Proc. SPIE 7346, Visual Analytics for Homeland Defense and Security, 73460E (30 April 2009); https://doi.org/10.1117/12.817769

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