22 April 2009 Target recognition: fusing long-wave infrared and electro-optical imagery for detection of humans in a scene
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Abstract
We aim to identify humans in multimodal imagery by predicting the human long-wave infrared (LWIR) signature in a variety of scenarios. By adapting Tanabe's thermocomfort model, we simulate human body heat flow both between tissue layers (core, muscle, fat and skin) and between body segments (head, chest, upper arm, etc.). To assess the validity of our implementation, we simulated the conditions described in actual human subject studies, and compared our results to values reported in the literature. Inputs to the model include age, height, weight, clothing, physical activity and ambient conditions, including temperature, humidity and wind velocity. Iteration of heat transport equations and a thermoregulatory component yields temporal data of segment surface temperature. Our model was found to be in close agreement with experimentally collected data, with a maximum deviation from literature values of approximately 0.80%. By comparing the predicted human thermal signature to deblurred LWIR images and then fusing this information at the feature level with high-resolution electro-optical image data, we can facilitate identity detection of objects in a scene acquired under different conditions. Ultimately, our goal is to differentiate humans from their surroundings and label non-human objects as thermal clutter.
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R. L. Woodyard, R. L. Woodyard, J. A. Skipper, J. A. Skipper, D. W. Repperger, D. W. Repperger, } "Target recognition: fusing long-wave infrared and electro-optical imagery for detection of humans in a scene", Proc. SPIE 7299, Thermosense XXXI, 72990I (22 April 2009); doi: 10.1117/12.821049; https://doi.org/10.1117/12.821049
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