A fundamental problem in imaging remote sensing systems is that of scale and resolution. The ability to resolve an object at a distance requires a high resolution sensor, with pixels subtending a small portion of the total field-of-view (FOV) of the imaging system. Traditional approaches to addressing this challenge are fundamentally data limited. To this end, we implemented foveating data reduction models inspired by the bi-foveated vision of birds of prey. The development of such systems for multiple target detection and tracking for air-to-ground target acquisition is important for several defense applications. The relative merits and disadvantages of various optical imaging technologies as well as several image transformations, sampling schemes, and object tracking algorithms were explored. Variable focal lens controlled by pressure, external voltage, or microfluidics demonstrate potential for devices requiring high resolution within a specified range. The distortion, coma, and spherical aberrations that occur can be corrected through the use of adaptive optics and custom 3D printed lenses. In conjunction with the hardware aspects, algorithmic approaches were also considered. The use of dynamically generated, moving foveal regions was investigated for use in motion tracking and object detection algorithms. Through the use of imaging systems with exceptionally large fields of view and localized areas of high resolution, machine vision systems can be implemented with less computational and data overhead. The implementation of our system is suited to use in either unmanned aerial vehicle or autonomous vehicle applications.