Compressive Sampling Video Sensor for Change Detection

InView and its close partner Rice University are world leaders in CS imaging algorithms and CS imaging sensor development. Over the last 8 years, there has been impressive progress on imaging architectures that seek to reduce the amount of data sensed by exploiting signal priors and task-specific imaging. We propose to leverage these latest advances in compressive sensing and computational imaging, many of which have been pioneered by the PIs and key personnel, to build imaging platforms tuned to change detection and the detection of fast moving objects such as rocket-propelled grenades. The three objectives of the Phase I efforts are to resolve the following CS change-detection challenges are to define reliable algorithms for rapid change detection, to define a small-form factor, low-cost hardware platform with sample speeds required to detect fast moving objects, and to perform system-level simulations to analyze the spectral, spatial and change-detection algorithms on a model of the platform. The Phase I work program that will meet the objectives listed above is focused on the preliminary design and feasibility demonstration of a multi-spectral infrared video sensor that uses advanced CS algorithms implemented on an innovative adaptive hardware platform.