Abstract
An object detection method needs a shadow detection because shadows often have a harmful effect on the result. Shadow detection methods based on shadow models are proposed. A new shadow model is proposed in this paper. The proposed model is constructed by the differences of UV components of YUV color space between the background image and the observed image, Normalized Vector Distance and edge information. The difference of Y component is less suitable for the model because it varies considerably with location. The proposed model includes Normalized Vector Distance instead of Y component. It is a robust feature to illumination changes and can remove a shadow effect in part. The proposed method can obtain shadow regions more accurately by including Normalized Vector Distance in the shadow model. Results are demonstrated by the experiments using the real videos.
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Ishida, S., Fukui, S., Iwahori, Y., Bhuyan, M.K., Woodham, R.J. (2013). Shadow Detection Method Based on Shadow Model with Normalized Vector Distance and Edge. In: Lee, R. (eds) Computer and Information Science. Studies in Computational Intelligence, vol 493. Springer, Heidelberg. https://doi.org/10.1007/978-3-319-00804-2_8
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DOI: https://doi.org/10.1007/978-3-319-00804-2_8
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