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- W2122105668 abstract "Statistical background modeling is a fundamental and important part of many visual tracking systems and of other computer vision applications. In this paper, we presents an effective and adaptive background modeling method for detecting foreground objects in both static and dynamic scenes. The proposed method computes SAmple CONsensus (SACON) of the background samples and estimates a statistical model per pixel. Numerous experiments on both indoor and outdoor video sequences show that the proposed method, compared with several state-of-the-art methods, can achieve very promising performance" @default.
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- W2122105668 date "2006-01-01" @default.
- W2122105668 modified "2023-09-25" @default.
- W2122105668 title "Background Subtraction Based on a Robust Consensus Method" @default.
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- W2122105668 doi "https://doi.org/10.1109/icpr.2006.312" @default.
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