Abstract
Cast shadow is the problem of moving cast detection in visual surveillance applications, which has been studied over years. However, finding an efficient model that can handle the issue of moving cast shadow in various situations is still challenging. Unlike prior methods, we use a data-driven method without the strong parametric assumptions or complex models to address the problem of moving cast shadow. In this paper, we propose a novel feature-extracting framework called Scale-Relation Feature Extracting (SRFE). By leveraging the scale space, SRFE decomposes each image with various properties into various scales and further considers the relationship between adjacent scales of the two shadow properties to extract the scale-relation features. To seek the criteria for discriminating moving cast shadow, we use random forest algorithm as the ensemble decision scheme. Experimental results show that the proposed method can achieve the performances of the popular methods on the widely used dataset.
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Acknowledgements
This work was supported in part by the Natural Science Foundation of Fujian Province of China, under grant 2016J01718.
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Lin, CW. (2017). Scale-Relation Feature for Moving Cast Shadow Detection. In: Amsaleg, L., Guðmundsson, G., Gurrin, C., Jónsson, B., Satoh, S. (eds) MultiMedia Modeling. MMM 2017. Lecture Notes in Computer Science(), vol 10133. Springer, Cham. https://doi.org/10.1007/978-3-319-51814-5_28
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DOI: https://doi.org/10.1007/978-3-319-51814-5_28
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