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- W2312654522 abstract "Background estimation in video consists in extracting a foreground-free image from a set of training frames. Moving and stationary objects may affect the background visibility, thus invalidating the assumption of many related literature where background is the temporal dominant data. In this paper, we present a temporal-spatial block-level approach for background estimation in video to cope with moving and stationary objects. First, a Temporal Analysis module obtains a compact representation of the training data by motion filtering and dimensionality reduction. Then, a threshold-free hierarchical clustering determines a set of candidates to represent the background for each spatial location (block). Second, a Spatial Analysis module iteratively reconstructs the background using these candidates. For each spatial location, multiple reconstruction hypotheses (paths) are explored to obtain its neighboring locations by enforcing inter-block similarities and intra-block homogeneity constraints in terms of color discontinuity, color dissimilarity and variability. The experimental results show that the proposed approach outperforms the related state-of-the-art over challenging video sequences in presence of moving and stationary objects." @default.
- W2312654522 created "2016-06-24" @default.
- W2312654522 creator A5025958097 @default.
- W2312654522 creator A5028186506 @default.
- W2312654522 creator A5030054394 @default.
- W2312654522 date "2016-06-01" @default.
- W2312654522 modified "2023-09-30" @default.
- W2312654522 title "Rejection based multipath reconstruction for background estimation in video sequences with stationary objects" @default.
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- W2312654522 doi "https://doi.org/10.1016/j.cviu.2016.03.012" @default.
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