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Proceedings Paper

Classification of humans and animals using an infrared profiling sensor
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Paper Abstract

This paper presents initial object profile classification results using range and elevation independent features from a simulated infrared profiling sensor. The passive infrared profiling sensor was simulated using a LWIR camera. A field data collection effort to yield profiles of humans and animals is reported. Range and elevation independent features based on height and width of the objects were extracted from profiles. The profile features were then used to train and test four classification algorithms to classify objects as humans or animals. The performance of Naïve Bayesian (NB), Naïve Bayesian with Linear Discriminant Analysis (LDA+NB), K-Nearest Neighbors (K-NN), and Support Vector Machines (SVM) are compared based on their classification accuracy. Results indicate that for our data set SVM and (LDA+NB) are capable of providing classification rates as high as 98.5%. For perimeter security applications where misclassification of humans as animals (true negatives) needs to be avoided, SVM and NB provide true negative rates of 0% while maintaining overall classification rates of over 95%.

Paper Details

Date Published: 5 May 2009
PDF: 9 pages
Proc. SPIE 7333, Unattended Ground, Sea, and Air Sensor Technologies and Applications XI, 733310 (5 May 2009); doi: 10.1117/12.819885
Show Author Affiliations
Srikant Chari, The Univ. of Memphis (United States)
Carl Halford, The Univ. of Memphis (United States)
Eddie Jacobs, The Univ. of Memphis (United States)
Forrest Smith, The Univ. of Memphis (United States)
Jeremy Brown, The Univ. of Memphis (United States)
David Russomanno, The Univ. of Memphis (United States)


Published in SPIE Proceedings Vol. 7333:
Unattended Ground, Sea, and Air Sensor Technologies and Applications XI
Edward M. Carapezza, Editor(s)

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