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Detecting shadows is needed for object detection methods because shadows often have a harmful effect on the result. Shadow detection methods based on shadow models are proposed. The shadow model should be updated to detect shadows which are not included in the learning data. When it is updated, outlier should be removed from them. In this paper, a data selection method for removing outlier from the data is proposed. The proposed method selects data by information of object regions and Normalized Distance. Using only the selected data results in obtaining better shadow regions because the better shadow model can be constructed. Results are demonstrated by the experiments using the real videos.
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