Abstract
Moving cast shadows on object distort figures which causes serious detection deficiency and analysis problems in ITS related applications. Thus, shadow removal plays an important role for robust object extraction from surveillance videos. In this paper, we propose an algorithm to eliminate moving cast shadow that uses features of color information about foreground and background figures. The significant information among the features of shadow, background and object is extracted by PCA transformation and tilting coordinates system. By appropriate analyses of the information, we found distributive characteristics of colors from the tilted PCA space. With this new color space, we can detect moving cast shadow and remove them effectively.
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Shin, W., Um, J., Song, D.H., Lee, C. (2007). Moving Cast Shadow Elimination Algorithm Using Principal Component Analysis in Vehicle Surveillance Video. In: Szczuka, M.S., et al. Advances in Hybrid Information Technology. ICHIT 2006. Lecture Notes in Computer Science(), vol 4413. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-77368-9_14
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DOI: https://doi.org/10.1007/978-3-540-77368-9_14
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-77367-2
Online ISBN: 978-3-540-77368-9
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