Deep Generative Modeling of Infrared Datasets for Aided Target Recognition

There is need for a generative suite of algorithms which can artificially generate militarily significant IR video that can be conditional modified to specific localities and target type to aid in the data hungry training of advanced machine learning tools for Aided Target Recognition (AiTR). To this end Lynntech proposes to develop a Synthetic InfraRed Video Suite (SIRVS) that is a cutting-edge fast deep-learning-based solution to this need that performs multi-frame IR video generative tasks jointly while also imposing seamless video continuity. The primary focus of the Phase I effort to develop a Simulation to Real (Sim2Real) tool that can enhance the realism of IR Video produces by the current Night Vision and Electronic Sensors Directorate (NVESD) artificial IR modeling systems, which is possible with an appropriate intermediate scene representation. The ultimate aim is to develop advanced generative tools that can extend the capability of current automated systems by fully leveraging the advantages of deep learning by the creation of synthetic IR video datasets through advanced data augmentation of existing IR data and modelling resources by either style transfer (i.e. changing time of day or the environment) or by coherently adding novel prescribed objects or targets to a video sequence.