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Proceedings Paper

Human thermal modeling to augment MWIR image analysis in surveillance applications
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Paper Abstract

The interpretation of thermal imagery can be augmented with information derived from human thermal modeling to better infer human activity during, or prior to, data capture. This additional insight into human activity could prove useful in security and surveillance applications. We have implemented Tanabe’s 65 NM thermocomfort model to predict skin surface temperature under a wide variety of environmental, activity and body parameters. Here, humans are modeled as sixteen segments (head, chest, upper leg, etc.), wherein spherical geometry is assumed for the head and cylindrical geometry is assumed for all other segments. Each segment is comprised of four layers: core, muscle, fat, and skin. Clothing is modeled as an additional layer (or layers) of resistance. Users supply input parameters via our custom MATLAB graphical user interface that includes a robust clothing database based on McCullough’s A Database for Determining the Evaporative Resistance of Clothing, and then Tanabe’s bioheat equations are solved to predict skin temperatures of each body segment. As an initial step of model validation, we compared our computed thermal resistances with literature values. Our evaporative and dry resistance on a per segment basis agreed with literature values. The dry resistance of each segment varied no more than .03 [m2°C/W]. Model validation will be extended to compare the results of our human subject trials (known body parameters, clothing, environmental factors and activity levels) to model outputs. Agreement would further substantiate the propagation of model- predicted skin temperatures through the thermal imager’s transfer function to predict human heat signatures in thermal imagery.

Paper Details

Date Published: 29 May 2014
PDF: 12 pages
Proc. SPIE 9075, Biometric and Surveillance Technology for Human and Activity Identification XI, 90750B (29 May 2014); doi: 10.1117/12.2050654
Show Author Affiliations
R. L. Woodyard, Wright State Univ. (United States)
J. A. Skipper, Wright State Univ. (United States)


Published in SPIE Proceedings Vol. 9075:
Biometric and Surveillance Technology for Human and Activity Identification XI
Ioannis A. Kakadiaris; Walter J. Scheirer; Christoph Busch, Editor(s)

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