Detection of moving foreground objects is essential to many image-sequence-analysis applications. However, preexisting methods tend to work best when the foreground is visually distinct from the background, suffering when objects are camouflaged. To address this shortcoming, a foreground-extraction algorithm resilient to camouflage is proposed by incorporating a redundant discrete wavelet transform into the well-known DECOLOR technique based on a sparse and low-rank model of the foreground-extraction problem. Detection of camouflaged moving objects is enhanced as a result of the combination of multiple background estimates in independent wavelet subbands into an overall estimate of the background, leveraging the known robustness of redundant wavelet transforms to additive noise. Experimental results demonstrate that the proposed method offers robustness to camouflage superior to that of other competing methods for image sequences containing snow leopards in the wild.