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• W2588478576 abstract "This paper proposes an efficient real-time background subtraction algorithm, which is essential in many computer vision applications. Initially, histograms of the intensity values for each channel of a pixel position in a set of training frames are constructed. A background model, histogram min-max bucket, is constructed from the minimum and maximum values of contiguous non-zero frequencies of the temporal intensity histogram. A novel feature of this algorithm is the use of a single sliding window to update the system adaptively, capturing the proper background even under sudden and/or gradual illumination changes in the scene. We incorporate both local and global information with the help of the current and the previously visited pixel values for binary classification of a pixel into foreground and background. This algorithm is compared with several state-of-the-art techniques and experimental studies show that the proposed method outperforms all these methods in terms of accurate binary classification." @default.
• W2588478576 created "2017-02-24" @default.
• W2588478576 creator A5000320119 @default.
• W2588478576 creator A5025656702 @default.
• W2588478576 date "2018-07-01" @default.
• W2588478576 modified "2023-09-27" @default.
• W2588478576 title "Real-Time Adaptive Histogram Min-Max Bucket (HMMB) Model for Background Subtraction" @default.
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• W2588478576 doi "https://doi.org/10.1109/tcsvt.2017.2669362" @default.
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