Abstract
Although researchers have proposed different kinds of techniques for background subtraction, we still need to produce more efficient algorithms in terms of adaptability to multimodal environments. We present a new background modeling algorithm based on temporal-local sample density outlier detection. We use the temporal-local densities of pixel samples as the decision measurement for background classification, with which we can deal with the dynamic backgrounds more efficiently and accurately. Experiment results have shown the outstanding performance of our proposed algorithm with multimodal environments.
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Acknowledgments
The work is supported by the Natural Science Foundation of Shandong Province under Grant No. ZR2014FM030, No. ZR2013FM032 and No. ZR2014FM010.
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Zeng, W., Yang, M., Wang, F., Cui, Z. (2017). Background Modeling Using Temporal-Local Sample Density Outlier Detection. In: Raman, B., Kumar, S., Roy, P., Sen, D. (eds) Proceedings of International Conference on Computer Vision and Image Processing. Advances in Intelligent Systems and Computing, vol 459. Springer, Singapore. https://doi.org/10.1007/978-981-10-2104-6_1
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DOI: https://doi.org/10.1007/978-981-10-2104-6_1
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