Wide-Area Surveillance with Multiple Cameras Using Distributed Compressive Imaging

In order to image a large area with a required resolution, a traditional camera would have to scan a smaller field-of-view until the entire area of interest is covered, thus losing persistence. Using a large sensor would result in high bandwidth data streams along with expensive and heavy equipment. Ideally, one would like to sense (or measure) a large number of pixels with a very limited set of measurements. In such a scenario the theory of compressive sensing may be put to use. A single sensor compressive imager for the wide area surveillance problem has been postulated and shown to be effective in detecting moving targets in a wide area. In this paper we look at the compressive imaging problem by assuming we have multiple cameras at our disposal. We show that we can get significant benefit in image reconstruction from multiple cameras measuring overlapped fields-of-view without any intra-camera communications and under significant transmission bandwidth constraints. We also show analysis and experiments which suggest that we can register these multiple cameras given only the random projective measurements from each camera.