9/27/2023 0 Comments Dynamic background![]() ![]() Especially, ViBe with the modifications outperforms some state-of-art algorithms presented on the CHANGEDETECTION website. ![]() Both GMM and CB with the proposed modifications have better performance than the original versions. Experiments on several popular public datasets prove the effectiveness and real-time performance of the proposed method. These two modifications are embedded in three classic background subtraction algorithms: probability based background subtraction (Gaussian mixture model, GMM), sample based background subtraction (visual background extractor, ViBe) and code words based background subtraction (code book, CB). ![]() This paper presents two universal modifications for pixel-wise foreground/background segmentation: dynamic background estimation and complementary learning. Since most outdoor surveillance videos are taken in native and complex environments, these “static” backgrounds change in some unknown patterns, which make perfect foreground extraction very difficult. Change and motion detection plays a basic and guiding role in surveillance video analysis. ![]()
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