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Illumination changes present challenging problems to video surveillance algorithms tasked with identifying and tracking objects. Illumination changes can drastically alter the appearance of a scene, causing truly salient features to be lost amid otherwise stable background. We describe an illumination change compensation method that identifies large, stable, chromatically distinct background features-called BigBackground regions - which are used as calibration anchors for scene correction. The benefits of this method are demonstrated for a computationally low-cost kinematic tracking application as it attempts to track objects during illumination changes. The BigBackground-based method is compared with other compensation techniques, and is found to successfully track 60% to 80% more objects during illumination changes. Video sequences of pedestrian and vehicular traffic are used for evaluation.