We present a method for retrieving illuminant spectra from a set of images taken with a fixed location camera, such as a surveillance or panoramic one. In these images, there will be significant changes in lighting conditions and scene content, but there will also be static elements in the background. As color constancy is an under-determined problem, we propose to exploit the redundancy and constancy offered by the static image elements to reduce the dimensionality of the problem. Specifically, we assume that the reflectance properties of these objects remain constant across the images taken with a given fixed camera. We demonstrate that we can retrieve illuminant and reflectance spectra in this framework by modeling the redundant image elements as a set of synthetic RGB patches. We define an error function that takes the RGB patches and a set of test illuminants as input and returns a similarity measure of the redundant surfaces reflectances. The test illuminants are then varied until the error function is minimized, returning the illuminants under which each image in the set was captured. This is achieved by gradient descent, providing an optimization method that is robust to shot noise.