Abstract
Moving shadow detection is an important technique for the integrity of object detection. This paper is based on the assumption that the shadow area is darker than the corresponding background area but keeps color constancy and texture consistency. The main contributions of this paper include two parts. First, an adaptive mechanism for shadow detection is proposed using texture of improved local ternary pattern. The main idea is to detect partial real shadows to estimate relative accurate threshold parameters for shadow detector. Second, we utilize a model of genetic programming model to fuse multiple features: texture, color, and gradient information. Experimental results on indoor and outdoor sequences demonstrated that the proposed method outperforms some state-of-the-art methods.
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Lin, Y., Wang, B., Zhao, Y. (2018). Moving Shadow Detection Using Fusion of Multiple Features. In: Bhatia, S., Mishra, K., Tiwari, S., Singh, V. (eds) Advances in Computer and Computational Sciences. Advances in Intelligent Systems and Computing, vol 554. Springer, Singapore. https://doi.org/10.1007/978-981-10-3773-3_47
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DOI: https://doi.org/10.1007/978-981-10-3773-3_47
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