Abstract
Moving cast shadow detection is an essential and pivotal problem in image processing. One problem is that many existed moving cast shadow detection methods are only suitable for some specific situations. In order to solve this problem, a cast shadow detection and elimination algorithm based on the combination of texture feature and YUV color space is proposed in this paper. Firstly, we detect moving object using PBAS algorithm which suppress ghost region. Furthermore, a part of cast shadow candidate region is obtained by texture detection. Then, the other cast shadow candidate region is obtained by shadow detection based on YUV color space. Finally, two parts of the cast shadow candidate regions are screened and merged by the shadow feature. Experimental results show that the proposed method has higher detection rates and discrimination rates compared to some well-known methods.
Z.-L. Sun—The work was supported by a grant from National Natural Science Foundation of China (No. 61370109), a key project of support program for outstanding young talents of Anhui province university (No. gxyqZD2016013), a grant of science and technology program to strengthen police force (No. 1604d0802019), and a grant for academic and technical leaders and candidates of Anhui province (No. 2016H090).
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Zheng, C., Sun, ZL., Wang, N., Bao, XY. (2018). Moving Cast Shadow Removal Based on Texture Feature and Color Space. In: Huang, T., Lv, J., Sun, C., Tuzikov, A. (eds) Advances in Neural Networks – ISNN 2018. ISNN 2018. Lecture Notes in Computer Science(), vol 10878. Springer, Cham. https://doi.org/10.1007/978-3-319-92537-0_70
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DOI: https://doi.org/10.1007/978-3-319-92537-0_70
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