Abstract: A change detection scheme used to detect objects in a complex real-life scene must be able to deal with illumination changes, shadows and structural variations of the environment. Several approaches are based on subtracting a reference image, representing the background, from the current input image. The most used methods estimate the background image by applying some low-pass filter on the input image sequence. Many of them require an accurate calibration phase and rely on a careful selection of critical parameters. An algorithm based on Kalman filtering is suggested here to dynamically estimate the background reference image. The approach extends former works and faces the severe problems of parameter tuning and modeling approximations. An experimental analysis on the behavior of the proposed algorithm in presence of different illumination changes is performed using noisy synthetic data. The results are used to address the choice of values for the filter parameters. The effectiveness and robustness of the algorithm are evaluated on several tests that were carried out on real-life sequences.
Keywords: Change detection; Figure-ground segmentation; Background estimation; Adaptive filtering; Divergence control
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