BigBackground-Based Illumination Compensation for Surveillance Video

Abstract

Abstract Illumination changes cause challenging problems for video surveillance algorithms, as objects of interest become masked by changes in background appearance. It is desired for such algorithms to maintain a consistent perception of a scene regardless of illumination variation. This work introduces a concept we call BigBackground, which is a model for representing large, persistent scene features based on chromatic self-similarity. This model is found to comprise 50% to 90% of surveillance scenes. The large, stable regions represented by the model are used as reference points for performing illumination compensation. The presented compensation technique is demonstrated to decrease improper false-positive classification of background pixels by an average of 83% compared to the uncompensated case and by 25% to 43% compared to compensation techniques from the literature.