Abstract
In the machine vision, the algorithm of background subtraction is used in target detection widely. In this paper, we reproduce some algorithms such as ViBe, Local Binary Similar Pattern (LBSP), Local Ternary Pattern (LTP) and so on. In view of the problem of inaccurate edge in target detection, we propose a method of combining color and LBSP for background model. It can obtain information both in pixel and texture (marked as improved-LBSP in the paper). On this basis, we propose a new method marked as BFs-method in the paper, which have a new persistence consists of color, LBSP, and time (t). The key advantage of this method lie in its highly robust dictionary model as well as it’s ability to automatically adjust pixel-level segmentation behavior, which improves the ability to remove the shadow of the target and the hole inside the target.
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This work is supported by the Research Fund of State Key Laboratory of High Performance Computing under Grant No. 201612-01, National University of Defense Technology.
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Zhang, S., Jiang, T., Peng, Y., Peng, X. (2017). A New Pixel-Level Background Subtraction Algorithm in Machine Vision. In: Huang, Y., Wu, H., Liu, H., Yin, Z. (eds) Intelligent Robotics and Applications. ICIRA 2017. Lecture Notes in Computer Science(), vol 10463. Springer, Cham. https://doi.org/10.1007/978-3-319-65292-4_45
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DOI: https://doi.org/10.1007/978-3-319-65292-4_45
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