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- W2023821762 abstract "A background estimation system, which integrates pixel-level features with a region-level one and combines short-term and long-term analysis of videos in outdoor illumination variations, is proposed for accurate foreground detection. Firstly, we discuss autocorrelation-based features for identification of the presence of foreground and outdoor illumination variations in short-term sequences, and propose an adaptive threshold learning approach insensitive to inner-pixel fast illumination variation based on histograms of intensity differences between successive frames. Then, we employ a pixel-wise rapid autoregressive model against gradual illumination change for background estimation in long-term sequence. Finally, we devise a texture measure to eliminate the regional effect of fast illumination variation. The effectiveness of our system is demonstrated using experiments on foreground detection in videos with various illumination changes." @default.
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- W2023821762 date "2011-11-01" @default.
- W2023821762 modified "2023-09-23" @default.
- W2023821762 title "Adaptive background estimation of outdoor illumination variations for foreground detection" @default.
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- W2023821762 doi "https://doi.org/10.1109/vcip.2011.6115943" @default.
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