Generation of synthetic high-speed video with radiometric fidelity and dynamic scene representation

Large representative datasets are essential for developing and testing tracking and detection algorithms. However, acquiring real-world data across a desired target-feature trade space can be challenging. Synthetic data generation can address these challenges if it provides radiometric fidelity, flexibility in sensor, target, and scene attributes, realistic dynamic conditions, and relevant noise sources. This paper presents a toolkit for generating synthetic high-speed video that meets these criteria. We describe our approach through a case study simulating an infrared sensor in geostationary orbit observing dynamic targets against an Earth background. The toolkit's capabilities are assessed by comparing simulation results with first-principles radiometric calculations and validating details such as target motion, signal-to-noise ratio (SNR), and scene clutter. Our toolkit fulfills a community need by generating realistic high-speed video, enabling rapid development and testing of algorithms while reducing costs and lead times associated with acquiring real-world data.