@article{boulmerka_foreground_2018,

title = {Foreground Segmentation in Videos Combining General Gaussian Mixture Modeling and Spatial Information},

author = {A. Boulmerka and M. S. Allili},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85048221613&doi=10.1109%2fTCSVT.2017.2665970&partnerID=40&md5=d086a278aff4b9776c8c9a38bc95087b},

doi = {10.1109/TCSVT.2017.2665970},

issn = {10518215},

year  = {2018},

date = {2018-01-01},

journal = {IEEE Transactions on Circuits and Systems for Video Technology},

volume = {28},

number = {6},

pages = {1330–1345},

abstract = {We present a new statistical approach combining temporal and spatial information for robust online background subtraction (BS) in videos. Temporal information is modeled by coupling finite mixtures of generalized Gaussian distributions with foreground/background co-occurrence analysis. Spatial information is modeled by combining multiscale inter-frame correlation analysis and histogram matching. We propose an online algorithm that efficiently fuses both information to cope with several BS challenges, such as cast shadows, illumination changes, and various complex background dynamics. In addition, global video information is used through a displacement measuring technique to deal with pan-tilt-zoom camera effects. Experiments with comparison with recent state-of-the-art methods have been conducted on standard data sets. Obtained results have shown that our approach surpasses several state-of-the-art methods on the aforementioned challenges while maintaining comparable computational time. © 2017 IEEE.},

note = {Publisher: Institute of Electrical and Electronics Engineers Inc.},

keywords = {Background subtraction, Cast shadow, Co-occurrence, Dynamic background, Mixture model, Networks (circuits), Pan tilt zooms, temporal/spatial information, Video signal processing},

pubstate = {published},

tppubtype = {article}

}

@article{allili_finite_2008,

title = {Finite general Gaussian mixture modeling and application to image and video foreground segmentation},

author = {M. S. Allili and N. Bouguila and D. Ziou},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-78149489170&doi=10.1117%2f1.2898125&partnerID=40&md5=8a9b3060dda2366f17b22a06606a9f09},

doi = {10.1117/1.2898125},

issn = {10179909},

year  = {2008},

date = {2008-01-01},

journal = {Journal of Electronic Imaging},

volume = {17},

number = {1},

abstract = {We propose a new finite mixture model based on the formalism of general Gaussian distribution (GGD). Because it has the flexibility to adapt to the shape of the data better than the Gaussian, the GGD is less prone to overfitting the number of mixture classes when dealing with noisy data. In the first part of this work, we propose a derivation of the maximum likelihood estimation for the parameters of the new mixture model, and elaborate an information-theoretic approach for the selection of the number of classes. In the second part, we validate the proposed model by comparing it to the Gaussian mixture in applications related to image and video foreground segmentation © 2008 SPIE and IS&T.},

keywords = {Finite mixture models, Foreground segmentation, Gaussian distribution, Gaussian mixture modeling, Gaussian mixtures, Gaussians, General Gaussian distribution, Image segmentation, Information theory, Information-theoretic approach, Maximum likelihood estimation, Mixture model, Mixtures, Noisy data, Overfitting},

pubstate = {published},

tppubtype = {article}

}