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.
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.