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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| Vydáno v: | IET computer vision Ročník 10; číslo 8; s. 884 - 893 |
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| Médium: | Journal Article |
| Jazyk: | angličtina |
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The Institution of Engineering and Technology
01.12.2016
Wiley |
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| ISSN: | 1751-9632, 1751-9640, 1751-9640 |
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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 |
| Author_xml | – sequence: 1 givenname: Junhua surname: Yan fullname: Yan, Junhua email: yjh9758@126.com organization: College of Astronautics, Nanjing University of Aeronautics and Astronautics, Nanjing, People's Republic of China – sequence: 2 givenname: Shunfei surname: Wang fullname: Wang, Shunfei organization: College of Astronautics, Nanjing University of Aeronautics and Astronautics, Nanjing, People's Republic of China – sequence: 3 givenname: Tianxia surname: Xie fullname: Xie, Tianxia organization: College of Astronautics, Nanjing University of Aeronautics and Astronautics, Nanjing, People's Republic of China – sequence: 4 givenname: Yong surname: Yang fullname: Yang, Yong organization: College of Astronautics, Nanjing University of Aeronautics and Astronautics, Nanjing, People's Republic of China – sequence: 5 givenname: Jiayi surname: Wang fullname: Wang, Jiayi organization: College of Astronautics, Nanjing University of Aeronautics and Astronautics, Nanjing, People's Republic of China |
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| Cites_doi | 10.1109/ICPR.2004.1333992 10.1109/ICPR.2000.905341 10.1016/j.ijleo.2013.08.034 10.1007/978-3-540-75757-3_35 10.1109/CVPRW.2014.126 10.1007/s11390-010-9355-8 10.1109/CVPR.1999.784637 10.1109/WACV.2014.6836059 10.1109/FPT.2012.6412132 10.1109/ICSMC.2004.1400815 10.1007/s00180-011-0246-4 10.1016/j.protcy.2012.03.021 10.1109/ICPR.2010.188 10.1109/TAC.2006.884922 10.1109/TIP.2004.836169 10.1109/CVPRW.2014.68 10.1109/TPAMI.2006.68 |
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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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| 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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