DTIC ADA564997: Multi Sensory Features for Personnel pdf

DTIC ADA564997: Multi Sensory Features for Personnel

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Personnel detection at border crossings has become an important issue recently. To reduce the number of false alarms, it is important to discriminate between humans and four-legged animals. This paper proposes using enhanced summary autocorrelation patterns for feature extraction from seismic sensors, a multi-stage exemplar selection framework to learn acoustic classifier, and temporal patterns from ultrasonic sensors. We compare the results using decision fusion with Gaussian Mixture Model classifiers and feature fusion with Support Vector Machines. From experimental results, we show that our proposed methods improve the robustness of the system

• Creator/s: Defense Technical Information Center
• Date: 7/8/2011
• Year: 2011
• Book Topics/Themes: DTIC Archive, , ILLINOIS UNIV AT URBANA-CHAMPAIGN, *BORDER SECURITY, *PERSONNEL DETECTION, CROSSINGS, DETECTORS, FEATURE EXTRACTION, HUMANS, SEISMOMETERS, SENSES(PHYSIOLOGY), SENSOR FUSION, ULTRASONICS, VECTOR ANALYSIS, WARNING SYSTEMS

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