Abstract
For many tracking and surveillance applications, Gaussian mixture model (GMM) provides an effective mean to segment the foreground from background. Though, because of insufficient and noisy data in complex dynamic scenes, the estimated parameters of the GMM, which are based on the assumption that the pixel process meets multi-modal Gaussian distribution, may not accurately reflect the underlying distribution of the observations. And the existing block-based GMM (BGMM) method may be able to segment only rough foreground objects with time-consuming calculations. To solve these difficulties, this paper proposes to use type-2 fuzzy sets (T2FSs) to handle GMM’s uncertain parameters (T2GMM). Furthermore, this paper also introduces a novel representation of contextual spatial information including the color, edge and texture features for each block which is faster and almost lossless (T2BGMM). Experimental results demonstrate the efficiency of the proposed methods.
Preview
Unable to display preview. Download preview PDF.
References
Zhong, F., Qin, X., Peng, Q.: Transductive segmentation of live video with non-stationary background. In: CVPR 2010, pp. 2189–2196. IEEE Press, San Francisco (2010)
Kita, Y.: Background modeling by combining joint intensity histogram with time-sequential data. In: ICPR 2010, pp. 991–994. IEEE Press, Istanbul (2010)
Lim, C.H., Vats, E., Chan, C.S.: Fuzzy human motion analysis. Pattern Recogn. 48(5), 1773–1796 (2015)
Zivkovic, Z., van der Heijden, F.: Recursive unsupervised learning of finite mixture models. PAMI 26(5), 651–656 (2004)
Fang, X.H., Xiong, W., Hu, B.J., Wang, L.T.: A moving object detection algorithm based on color information. Journal of Physics: Conference Series 48(1), 384–387 (2006)
Li, L., Huang, W., Gu, I.Y.H., Tian, Q.: Foreground object detection from videos containing complex background. In: Multimedia Proceedings of the Eleventh ACM International Conference on Multimedia, pp. 2–10. ACM, New York (2003)
Zeng, J., Xie, L., Liu, Z.Q.: Type-2 fuzzy Gaussian mixture models. Pattern Recognition 41(12), 3636–3643 (2008)
Mendel, J.M.: Type-2 fuzzy sets and systems: an overview. Comp. Intell. Mag. 2(1), 20–29 (2007)
Baf, F.E., Bouwmans, T., Vachon, B.: Fuzzy statistical modeling of dynamic backgrounds for moving object detection in infrared videos. In: OTCBVS, pp. 60–65. IEEE Press, Miami (2009)
Shen, B., Sethi, I.K.: Direct feature extraction from compressed images. In: SPIE: Storage and Retrieval for Image and Video Databases IV, vol. 2670, pp. 404–414 (1996)
Stauffer, C., Grimson, W.E.L.: Learning patterns of activity using real-time tracking. PAMI 22(8), 747–757 (2000)
Reddy, V., Sanderson, C., Lovell, B.C.: Robust foreground object segmentation via adaptive region-based background modelling. In: ICPR, pp. 3939–3942. IEEE Press, Istanbul (2010)
Yeo, B.C., Lim, W.S., Lim, H.S.: Scalable-width temporal edge detection for recursive background recovery in adaptive background modeling. Appl. Soft Comput. 13(4), 1583–1591 (2013)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Guo, Y., Ji, Y., Zhang, J., Gong, S., Liu, C. (2015). Robust Dynamic Background Model with Adaptive Region Based on T2FS and GMM. In: Zhang, S., Wirsing, M., Zhang, Z. (eds) Knowledge Science, Engineering and Management. KSEM 2015. Lecture Notes in Computer Science(), vol 9403. Springer, Cham. https://doi.org/10.1007/978-3-319-25159-2_70
Download citation
DOI: https://doi.org/10.1007/978-3-319-25159-2_70
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-25158-5
Online ISBN: 978-3-319-25159-2
eBook Packages: Computer ScienceComputer Science (R0)