Abstract
The detection of foreground regions in video streams is an essential part of many computer vision algorithms. Considerable contributions were made to this field over the past years. However, varying illumination circumstances and changing camera viewpoints provide major challenges for all available algorithms. In this paper, a robust foreground background segmentation algorithm is proposed. Both Local Ternary Pattern based edge descriptors and RGB color information are used to classify individual pixels. Furthermore, camera viewpoints are detected and compensated for. We will show that this algorithm is able to handle challenging conditions and achieves state-of-the-art results on the comprehensive ChangeDetection.NET 2014 dataset.
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Acknowledgements
We would like to thank the creators of ChangeDetection.NET and all those responsible for providing the means to evaluate our foreground background estimation algorithm on this very comprehensive dataset.
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Allebosch, G., Van Hamme, D., Deboeverie, F., Veelaert, P., Philips, W. (2016). C-EFIC: Color and Edge Based Foreground Background Segmentation with Interior Classification. In: Braz, J., et al. Computer Vision, Imaging and Computer Graphics Theory and Applications. VISIGRAPP 2015. Communications in Computer and Information Science, vol 598. Springer, Cham. https://doi.org/10.1007/978-3-319-29971-6_23
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DOI: https://doi.org/10.1007/978-3-319-29971-6_23
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