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Proceedings Paper

Range and velocity independent classification of humans and animals using a profiling sensor
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Paper Abstract

This paper presents object profile classification results using range and speed independent features from an infrared profiling sensor. The passive infrared profiling sensor was simulated using a LWIR camera. Field data collected near the US-Mexico border to yield profiles of humans and animals is reported. Range and speed independent features based on height and width of the objects were extracted from profiles. The profile features were then used to train and test three classification algorithms to classify objects as humans or animals. The performance of Naïve Bayesian (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 all three algorithms achieve classification rates of over 98%. The field data is also used to validate our prior data collections from more controlled environments.

Paper Details

Date Published: 7 May 2010
PDF: 8 pages
Proc. SPIE 7694, Ground/Air Multi-Sensor Interoperability, Integration, and Networking for Persistent ISR, 76941K (7 May 2010); doi: 10.1117/12.861767
Show Author Affiliations
Srikant Chari, The Univ. of Memphis (United States)
Forrest Smith, The Univ. of Memphis (United States)
Carl Halford, The Univ. of Memphis (United States)
Eddie Jacobs, The Univ. of Memphis (United States)
Jason Brooks, The Univ. of Memphis (United States)


Published in SPIE Proceedings Vol. 7694:
Ground/Air Multi-Sensor Interoperability, Integration, and Networking for Persistent ISR
Michael A. Kolodny, Editor(s)

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