Abstract
Background subtraction or also called foreground detection is an approach normally used for moving object segmentation in video sequences captured from a fixed camera. Most of the methods under this approach are not able to segment or require the strict absence of objects during their training or learning period in the first frames. In this document, a method capable of segmenting moving objects from the beginning of a video sequence and at the same time constructing a reference background image is proposed. The segmentation results show that the foreground and background regions of the scene are not affected during this stage compared to other methods.
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References
Maddalena, L., Petrosino, A.: A self-organizing approach to background subtraction for visual surveillance applications. IEEE Trans. Image Process. 17 (2008)
Cheung, S.-C.S., Kamath, C.: Robust techniques for background subtraction in urban traffic video. In: Proceedings SPIE 5308, Visual Communications and Image Processing (2004)
Buch, N., Velastin, S.A., Orwell, J.: A review of computer vision techniques for the analysis of urban traffic. IEEE Trans. Intell. Transp. Syst. 12 (2011)
Wren, C.R., Azarbayejani, A.: Pfinder: real-time tracking of the human body. IEEE Trans. Intell. Transp. Syst. 19 (1997)
Piccardi, M.: Background subtraction techniques: a review. In: IEEE International Conference on Systems, Man and Cybernetics (2004)
Engel, J.I., Martín, J., Barco, R.: A low-complexity vision-based system for real-time traffic monitoring. IEEE Trans. Intell. Transp. Syst. 18 (2016)
McFarlane, N.J.B., Schofield, C.P.: Segmentation and tracking of piglets in images. In: Machine Vision and Applications, vol. 8. Springer-Verlag (1995)
Zeng, Z., Jia, J., Yu, D.: Pixel modeling using histograms based on fuzzy partitions for dynamic background subtraction. IEEE Trans. Fuzzy Syst. 25(3) (2017)
Stauffer, C., Grimson, W.E.L.: Learning patterns of activity using real-time tracking. IEEE Trans. Pattern Anal. Mach. Intell. 22 (2000)
Datondji, S.R.E., Dupuis, Y., Subirats, P.: A survey of vision-based traffic monitoring of road intersections. IEEE Trans. Intell. Transp. Syst. 17(10) (2016)
Zhong, Z., Zhang, B., Lu, G., Zhao, Y.: An adaptive background modeling method for foreground segmentation. IEEE Trans. Intell. Transp. Syst. 18(5) (2017)
Stauffer, C., Grimson, W.E.L.: Adaptive background mixture models for real-time tracking. In: Proceedings IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2 (1999)
Wang, K., Liu, Y., Gou, C.: A multi-view learning approach to foreground detection for traffic surveillance applications. IEEE Trans. Veh. Technol. 65(6) (2016)
Kim, H., Sakamoto, R., Kitahara, I., Toriyama, T., Kogure, K.: Background subtraction using generalised Gaussian family model. Electron. Lett. 44 (2008)
Wang, W., Yang, J., Gao, W.: Modeling background and segmenting moving objects from compressed video. IEEE Trans. Circuits Syst. Video Technol. 18 (2008)
Lee, H., Kim, H., Kim, J.-I.: Background subtraction using background sets with image- and color-space reduction. IEEE Trans. Multimed. 18(10) (2016)
Acknowledgment
We would like to thank the Instituto Politécnico Nacional and CONACyT from Mexico, undoubtedly the support from these institutions was vital in the accomplishment of this work.
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García, B.G., Funes, F.J.G., Silva, A.J.R. (2019). A Gaussian-Median Filter for Moving Objects Segmentation Applied for Static Scenarios. In: Arai, K., Kapoor, S., Bhatia, R. (eds) Intelligent Systems and Applications. IntelliSys 2018. Advances in Intelligent Systems and Computing, vol 868. Springer, Cham. https://doi.org/10.1007/978-3-030-01054-6_34
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DOI: https://doi.org/10.1007/978-3-030-01054-6_34
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