Abstract
This paper aims to analyze a background subtraction algorithm. Different from tradition methods, we feed the trained network with the target and background images. The paper focuses on how to get background images without using the temporal median filter. We use Gaussian mixture models to produce background images. In this way, the accuracy of background images increases. We also study the difference between grayscale and RGB images, and adding the foreground masks from the convolutional Neural Networks to the Gaussian mixture models. Experiments lead on the 2014 ChangeDetection.net dataset show that our proposed method outperforms several state-of-the-art methods, including IUTIS-5, PAWCS, SuBSENSE and so on.
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Acknowledgments
This work was supported in part by the Ministry of Science and Technology, Taiwan, R.O.C., under grant no. MOST 107-2221-E-126-005.
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Lin, TY., Yeh, JS., Wu, FC., Chuang, YY., Dellinger, A. (2019). An Experiment for Background Subtraction in a Dynamic Scene. In: Meurs, MJ., Rudzicz, F. (eds) Advances in Artificial Intelligence. Canadian AI 2019. Lecture Notes in Computer Science(), vol 11489. Springer, Cham. https://doi.org/10.1007/978-3-030-18305-9_39
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DOI: https://doi.org/10.1007/978-3-030-18305-9_39
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