Accession Number ADA564997
Title Multi-Sensory Features for Personnel Detection at Border Crossings.
Publication Date Jul 2011
Media Count 9p
Personal Author M. Hasegawa-Johnson P. Huang T. Damarla
Abstract 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.
Keywords Border security
Crossings
Detectors
Feature extraction
Humans
Personnel detection
Seismometers
Senses(Physiology)
Sensor fusion
Ultrasonics
Vector analysis
Warning systems


 
Source Agency Non Paid ADAS
NTIS Subject Category 91I - Emergency Services & Planning
92 - Behavior & Society
62F - Pattern Recognition & Image Processing
63F - Optical Detection
Corporate Author Illinois Univ. at Urbana-Champaign.
Document Type Technical report
Title Note Conference paper.
NTIS Issue Number 1304
Contract Number N/A

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