Radiometrically Accurate Thermal Vehicle Targets for Synthetic Video Development - Rhodes, David B (ProQuest Dissertations & Theses, 2016)
Collecting large scientific quality thermal infrared image and video data sets is an expen- sive time consuming endeavor. Thermal infrared imagers cost much more than comparable visible systems and require skilled experienced operators. Also, time and experienced per- sonnel are required to collect quality ground truth. Often it is advantageous to perform computer simulations as an alternative to collecting image and video data with real camera systems. As long as enough physics is incorporated into the models to give accurately comparable results to real imagery, simulated data can be used interchangeably. Generating synthetic images and video has the added benefit of being flexible as the user has control over every aspect of the simulation. Simulations are not subject to restrictions such as location, weather conditions, time of day, or time of year. Ground truth is assigned instead of measured in the synthetic world so it is known a priori. This thesis illustrates a method of using the Digital Image and Remote Sensing Image Generation (DIRSIG) software to create simulated infrared images and video of validated thermal target vehicle models inside thermal infrared wide-area scenes. A finite difference heat propagation and surface temperature solver, ThermoAnalytics Multi-Service Electro-optic Signature (MuSES ™), was used to accurately model the emissive thermal target vehicles. Validation of the ther- mal target vehicle model was performed using images taken from a laboratory calibrated MWIR camera. Images taken with the calibrated camera of the same type of vehicle as the target model were compared to the synthetic images for the same conditions for vali- dation. Target vehicle motion was added to the simulations through the use of Simulation of Urban Mobility (SUMO), DIRSIGs movement files, and custom python scripting. The output images from DIRSIG were then laced together into video. The resulting video was used to test three tracking algorithms illuminating each one’s strengths and weaknesses.