Variational Bayesian learning for background subtraction based on local fusion feature
To resist the adverse effect of shadow interference, illumination changes, indigent texture and scenario jitter in object detection and improve performance, a background modelling method based on local fusion feature and variational Bayesian learning is proposed. First, U-LBSP (uniform-local binary...
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| Published in: | IET computer vision Vol. 10; no. 8; pp. 884 - 893 |
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| Language: | English |
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The Institution of Engineering and Technology
01.12.2016
Wiley |
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| Abstract | To resist the adverse effect of shadow interference, illumination changes, indigent texture and scenario jitter in object detection and improve performance, a background modelling method based on local fusion feature and variational Bayesian learning is proposed. First, U-LBSP (uniform-local binary similarity patterns) texture feature, lab colour and location feature are used to construct local fusion feature. U-LBSP is modified from local binary patterns in order to reduce computational complexity and better resist the influence of shadow and illumination changes. Joint colour and location feature are introduced to deal with the problem of indigent texture and scenario jitter. Then, LFGMM (Gaussian mixture model based on local fusion feature) is updated and learned by variational Bayes. In order to adapt to dynamic changing scenarios, the variational expectation maximisation algorithm is applied for distribution parameters optimisation. In this way, the optimal number of Gaussian components as well as their parameters can be automatically estimated with less time expended. Experimental results show that the authors’ method achieves outstanding detection performance especially under conditions of shadow disturbances, illumination changes, indigent texture and scenario jitter. Strong robustness and high accuracy have been achieved. |
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| AbstractList | To resist the adverse effect of shadow interference, illumination changes, indigent texture and scenario jitter in object detection and improve performance, a background modelling method based on local fusion feature and variational Bayesian learning is proposed. First, U‐LBSP (uniform‐local binary similarity patterns) texture feature, lab colour and location feature are used to construct local fusion feature. U‐LBSP is modified from local binary patterns in order to reduce computational complexity and better resist the influence of shadow and illumination changes. Joint colour and location feature are introduced to deal with the problem of indigent texture and scenario jitter. Then, LFGMM (Gaussian mixture model based on local fusion feature) is updated and learned by variational Bayes. In order to adapt to dynamic changing scenarios, the variational expectation maximisation algorithm is applied for distribution parameters optimisation. In this way, the optimal number of Gaussian components as well as their parameters can be automatically estimated with less time expended. Experimental results show that the authors’ method achieves outstanding detection performance especially under conditions of shadow disturbances, illumination changes, indigent texture and scenario jitter. Strong robustness and high accuracy have been achieved. |
| Author | Yan, Junhua Xie, Tianxia Wang, Jiayi Wang, Shunfei Yang, Yong |
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| Keywords | Gaussian mixture model based on local fusion feature image fusion distribution parameter optimisation object detection variational expectation maximisation algorithm illumination changes shadow disturbances optimisation variational techniques image colour analysis learning (artificial intelligence) lab colour location feature LFGMM uniform-local binary similarity pattern texture feature U-LBSP texture feature Gaussian components background modelling method shadow interference effect indigent texture image texture scenario jitter jitter variational Bayesian learning expectation-maximisation algorithm Bayes methods background subtraction computational complexity |
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| References | Shen, Y.; Cornford, D.; Opper, M. (C16) 2012; 27 Li, L.; Huang, W.; Gu, I.Y.H. (C7) 2004; 13 Boyd, S.; Vandenberghe, L. (C20) 2006; 51 Hernandez-Lopez, J.J.; Quintanilla-Olvera, A.L.; Lopez-Ramirez, J.L. (C5) 2012; 3 Çaglar, A.; Aksoy, S.; Arikan, O. (C14) 2012; 45 Attias, H. (C19) 2000; 12 Heikkila, M.; Pietikainen, M. (C2) 2006; 28 Wang, B.; Liu, Y.; Xu, W. (C6) 2014; 125 Gorur, D.; Rasmussen, C.E. (C15) 2010; 25 2012; 3 2010; 25 2006; 51 2010 2000; 12 2006; 28 2004; 13 2007 2004 2000; 1 2015 2014 1999; 2 2004; 2 2012; 27 2013 2012; 45 2014; 125 e_1_2_8_16_2 e_1_2_8_17_2 e_1_2_8_18_2 e_1_2_8_12_2 e_1_2_8_23_2 e_1_2_8_13_2 e_1_2_8_14_2 e_1_2_8_9_2 Çaglar A. (e_1_2_8_15_2) 2012; 45 e_1_2_8_2_2 Attias H. (e_1_2_8_20_2) 2000; 12 e_1_2_8_4_2 e_1_2_8_3_2 e_1_2_8_6_2 e_1_2_8_5_2 e_1_2_8_8_2 e_1_2_8_7_2 e_1_2_8_10_2 e_1_2_8_21_2 e_1_2_8_11_2 e_1_2_8_22_2 Covoes T.F. (e_1_2_8_19_2) 2013 |
| References_xml | – volume: 13 start-page: 1459 issue: 11 year: 2004 end-page: 1472 ident: C7 article-title: Statistical modeling of complex backgrounds for foreground object detection publication-title: IEEE Trans. Image Process. – volume: 27 start-page: 149 issue: 1 year: 2012 end-page: 176 ident: C16 article-title: Variational Markov chain Monte Carlo for Bayesian smoothing of non-linear diffusions publication-title: Comput. Stat. – volume: 51 start-page: 1859 issue: 11 year: 2006 end-page: 1859 ident: C20 article-title: Convex optimization publication-title: IEEE Trans. Autom. Control – volume: 45 start-page: 2804 issue: 45 year: 2012 end-page: 2816 ident: C14 article-title: Maximum likelihood estimation of Gaussian mixture models using stochastic search publication-title: Pattern Recognit. – volume: 3 start-page: 196 issue: 1 year: 2012 end-page: 204 ident: C5 article-title: Detecting objects using color and depth segmentation with Kinect sensor publication-title: Procedia Technol. – volume: 125 start-page: 1406 issue: 3 year: 2014 end-page: 1411 ident: C6 article-title: Background subtraction using spatiotemporal condition information publication-title: Opt.-Int. J. Light Electron. Opt. – volume: 28 start-page: 657 issue: 4 year: 2006 end-page: 662 ident: C2 article-title: A texture-based method for modeling the background and detecting moving objects publication-title: IEEE Trans. PAMI – volume: 12 start-page: 209 year: 2000 end-page: 215 ident: C19 article-title: A variational Bayesian framework for graphical models publication-title: Adv. Neural Inf. Process. Syst. – volume: 25 start-page: 653 issue: 4 year: 2010 end-page: 664 ident: C15 article-title: Dirichlet process Gaussian mixture models: choice of the base distribution publication-title: J. Comput. Sci. Technol. – volume: 2 start-page: 28 year: 2004 end-page: 31 – volume: 12 start-page: 209 year: 2000 end-page: 215 article-title: A variational Bayesian framework for graphical models publication-title: Adv. Neural Inf. Process. Syst. – volume: 45 start-page: 2804 issue: 45 year: 2012 end-page: 2816 article-title: Maximum likelihood estimation of Gaussian mixture models using stochastic search publication-title: Pattern Recognit. – start-page: 286 year: 2007 end-page: 293 – volume: 3 start-page: 196 issue: 1 year: 2012 end-page: 204 article-title: Detecting objects using color and depth segmentation with Kinect sensor publication-title: Procedia Technol. – volume: 1 start-page: 321 year: 2000 end-page: 324 – volume: 2 year: 1999 – volume: 27 start-page: 149 issue: 1 year: 2012 end-page: 176 article-title: Variational Markov chain Monte Carlo for Bayesian smoothing of non‐linear diffusions publication-title: Comput. Stat. – start-page: 746 year: 2010 end-page: 749 – start-page: 393 year: 2014 end-page: 400 – volume: 28 start-page: 657 issue: 4 year: 2006 end-page: 662 article-title: A texture‐based method for modeling the background and detecting moving objects publication-title: IEEE Trans. PAMI – start-page: 3099 year: 2004 end-page: 3104 – volume: 13 start-page: 1459 issue: 11 year: 2004 end-page: 1472 article-title: Statistical modeling of complex backgrounds for foreground object detection publication-title: IEEE Trans. Image Process. – start-page: 3206 year: 2013 end-page: 3213 – volume: 25 start-page: 653 issue: 4 year: 2010 end-page: 664 article-title: Dirichlet process Gaussian mixture models: choice of the base distribution publication-title: J. Comput. Sci. Technol. – volume: 125 start-page: 1406 issue: 3 year: 2014 end-page: 1411 article-title: Background subtraction using spatiotemporal condition information publication-title: Opt.‐Int. J. Light Electron. Opt. – volume: 45 start-page: 182 year: 2012 end-page: 189 – volume: 51 start-page: 1859 issue: 11 year: 2006 end-page: 1859 article-title: Convex optimization publication-title: IEEE Trans. Autom. Control – start-page: 420 year: 2014 end-page: 424 – year: 2015 – start-page: 509 year: 2014 end-page: 515 – ident: e_1_2_8_13_2 doi: 10.1109/ICPR.2004.1333992 – volume: 12 start-page: 209 year: 2000 ident: e_1_2_8_20_2 article-title: A variational Bayesian framework for graphical models publication-title: Adv. Neural Inf. Process. Syst. – ident: e_1_2_8_9_2 doi: 10.1109/ICPR.2000.905341 – ident: e_1_2_8_7_2 doi: 10.1016/j.ijleo.2013.08.034 – ident: e_1_2_8_4_2 doi: 10.1007/978-3-540-75757-3_35 – volume: 45 start-page: 2804 issue: 45 year: 2012 ident: e_1_2_8_15_2 article-title: Maximum likelihood estimation of Gaussian mixture models using stochastic search publication-title: Pattern Recognit. – ident: e_1_2_8_22_2 doi: 10.1109/CVPRW.2014.126 – ident: e_1_2_8_16_2 doi: 10.1007/s11390-010-9355-8 – start-page: 3206 volume-title: IEEE Congress on Evolutionary Computation year: 2013 ident: e_1_2_8_19_2 – ident: e_1_2_8_12_2 doi: 10.1109/CVPR.1999.784637 – ident: e_1_2_8_5_2 doi: 10.1109/WACV.2014.6836059 – ident: e_1_2_8_18_2 doi: 10.1109/FPT.2012.6412132 – ident: e_1_2_8_2_2 doi: 10.1109/ICSMC.2004.1400815 – ident: e_1_2_8_17_2 doi: 10.1007/s00180-011-0246-4 – ident: e_1_2_8_6_2 doi: 10.1016/j.protcy.2012.03.021 – ident: e_1_2_8_14_2 doi: 10.1109/ICPR.2010.188 – ident: e_1_2_8_21_2 doi: 10.1109/TAC.2006.884922 – ident: e_1_2_8_8_2 doi: 10.1109/TIP.2004.836169 – ident: e_1_2_8_11_2 doi: 10.1109/CVPRW.2014.68 – ident: e_1_2_8_23_2 – ident: e_1_2_8_10_2 – ident: e_1_2_8_3_2 doi: 10.1109/TPAMI.2006.68 |
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| SubjectTerms | background modelling method background subtraction Bayes methods Bayesian analysis computational complexity distribution parameter optimisation expectation-maximisation algorithm Gaussian components Gaussian mixture model based on local fusion feature Illumination illumination changes image colour analysis image fusion image texture indigent texture Jitter lab colour learning (artificial intelligence) LFGMM location feature Mathematical models object detection optimisation Research Article Resists scenario jitter shadow disturbances shadow interference effect Shadows Surface layer Texture U-LBSP texture feature uniform-local binary similarity pattern texture feature variational Bayesian learning variational expectation maximisation algorithm variational techniques |
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| Title | Variational Bayesian learning for background subtraction based on local fusion feature |
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