Slide
Centre Interdisciplinaire
de Recherche et d’Innovation
en Cybersécurité et Société
de Recherche et d’Innovation
en Cybersécurité et Société
1.
Boulmerka, A.; Allili, M. S.
Foreground Segmentation in Videos Combining General Gaussian Mixture Modeling and Spatial Information Journal Article
In: IEEE Transactions on Circuits and Systems for Video Technology, vol. 28, no. 6, pp. 1330–1345, 2018, ISSN: 10518215, (Publisher: Institute of Electrical and Electronics Engineers Inc.).
Abstract | Links | BibTeX | Tags: Background subtraction, Cast shadow, Co-occurrence, Dynamic background, Mixture model, Networks (circuits), Pan tilt zooms, temporal/spatial information, Video signal processing
@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}
}
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.
2.
Allili, M. S.; Ziou, D.
Adaptive appearance model for object contour tracking in videos Proceedings Article
In: Proceedings - Fourth Canadian Conference on Computer and Robot Vision, CRV 2007, pp. 510–517, Montreal, QC, 2007, ISBN: 0769527868 (ISBN); 978-076952786-4 (ISBN), (Journal Abbreviation: Proc. Fourth Can. Conf. Comput. Robot Vis.).
Abstract | Links | BibTeX | Tags: Adaptive parametric mixture models, Adaptive systems, Boundary, Color, Geometry, Image communication systems, Level sets, Level-sets, Mathematical models, Mixture of pdfs, Object mixture modelss, Pattern matching, Shape, Target tracking, Texture, Tracking, Variational techniques, Video sequences, Video signal processing
@inproceedings{allili_adaptive_2007,
title = {Adaptive appearance model for object contour tracking in videos},
author = {M. S. Allili and D. Ziou},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-34548723968&doi=10.1109%2fCRV.2007.9&partnerID=40&md5=c10008163f7f45f743ef0dcb13444c72},
doi = {10.1109/CRV.2007.9},
isbn = {0769527868 (ISBN); 978-076952786-4 (ISBN)},
year = {2007},
date = {2007-01-01},
booktitle = {Proceedings - Fourth Canadian Conference on Computer and Robot Vision, CRV 2007},
pages = {510–517},
address = {Montreal, QC},
abstract = {In this paper, we propose a novel object tracking algorithm in video sequences. The formulation of the object tracking is based on variational calculus, where an adaptive parametric mixture model is used for object features representation. The tracking is based on matching the object mixture models between successive frames of the sequence by using active contours while adapting the mixture model to varying object appearance changes due to illumination conditions and camera geometry. The implementation of the method is based on level set active contours which allow for automatic topology changes and stable numerical schemes. We validate our approach on examples of object tracking performed on real video sequences. © 2007 IEEE.},
note = {Journal Abbreviation: Proc. Fourth Can. Conf. Comput. Robot Vis.},
keywords = {Adaptive parametric mixture models, Adaptive systems, Boundary, Color, Geometry, Image communication systems, Level sets, Level-sets, Mathematical models, Mixture of pdfs, Object mixture modelss, Pattern matching, Shape, Target tracking, Texture, Tracking, Variational techniques, Video sequences, Video signal processing},
pubstate = {published},
tppubtype = {inproceedings}
}
In this paper, we propose a novel object tracking algorithm in video sequences. The formulation of the object tracking is based on variational calculus, where an adaptive parametric mixture model is used for object features representation. The tracking is based on matching the object mixture models between successive frames of the sequence by using active contours while adapting the mixture model to varying object appearance changes due to illumination conditions and camera geometry. The implementation of the method is based on level set active contours which allow for automatic topology changes and stable numerical schemes. We validate our approach on examples of object tracking performed on real video sequences. © 2007 IEEE.
3.
Allili, M. S.; Bouguila, N.; Ziou, D.
A robust video foreground segmentation by using generalized gaussian mixture modeling Proceedings Article
In: Proceedings - Fourth Canadian Conference on Computer and Robot Vision, CRV 2007, pp. 503–509, Montreal, QC, 2007, ISBN: 0769527868 (ISBN); 978-076952786-4 (ISBN), (Journal Abbreviation: Proc. Fourth Can. Conf. Comput. Robot Vis.).
Abstract | Links | BibTeX | Tags: Bayesian networks, Gaussian mixtures, Image segmentation, Mathematical models, Mixture of general gaussians (MoGG), MML, Video foreground segmentation, Video signal processing
@inproceedings{allili_robust_2007,
title = {A robust video foreground segmentation by using generalized gaussian mixture modeling},
author = {M. S. Allili and N. Bouguila and D. Ziou},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-34548763871&doi=10.1109%2fCRV.2007.7&partnerID=40&md5=e720d61af262e01677a3e23d8c4e6ad0},
doi = {10.1109/CRV.2007.7},
isbn = {0769527868 (ISBN); 978-076952786-4 (ISBN)},
year = {2007},
date = {2007-01-01},
booktitle = {Proceedings - Fourth Canadian Conference on Computer and Robot Vision, CRV 2007},
pages = {503–509},
address = {Montreal, QC},
abstract = {In this paper, we propose a robust video foreground modeling by using a finite mixture model of generalized Gaussian distributions (GDD). The model has a flexibility to model the video background in the presence of sudden illumination changes and shadows, allowing for an efficient foreground segmentation. In a first part of the present work, we propose a derivation of the online estimation of the parameters of the mixture of GDDS and we propose a Bayesian approach for the selection of the number of classes. In a second part, we show experiments of video foreground segmentation demonstrating the performance of the proposed model. © 2007 IEEE.},
note = {Journal Abbreviation: Proc. Fourth Can. Conf. Comput. Robot Vis.},
keywords = {Bayesian networks, Gaussian mixtures, Image segmentation, Mathematical models, Mixture of general gaussians (MoGG), MML, Video foreground segmentation, Video signal processing},
pubstate = {published},
tppubtype = {inproceedings}
}
In this paper, we propose a robust video foreground modeling by using a finite mixture model of generalized Gaussian distributions (GDD). The model has a flexibility to model the video background in the presence of sudden illumination changes and shadows, allowing for an efficient foreground segmentation. In a first part of the present work, we propose a derivation of the online estimation of the parameters of the mixture of GDDS and we propose a Bayesian approach for the selection of the number of classes. In a second part, we show experiments of video foreground segmentation demonstrating the performance of the proposed model. © 2007 IEEE.
4.
Allili, M. S.; Ziou, D.
Object contour tracking in videos by matching finite mixture models Proceedings Article
In: Proceedings - IEEE International Conference on Video and Signal Based Surveillance 2006, AVSS 2006, Sydney, NSW, 2006, ISBN: 0-7695-2688-8 978-0-7695-2688-1.
Abstract | Links | BibTeX | Tags: Boundary conditions, Color image processing, Contour measurement, Finite mixture models, Image analysis, Level sets, Object contour tracking, Pattern matching, Shape information, Video signal processing
@inproceedings{allili_object_2006,
title = {Object contour tracking in videos by matching finite mixture models},
author = {M. S. Allili and D. Ziou},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-34547433254&doi=10.1109%2fAVSS.2006.83&partnerID=40&md5=aeed9598e325243f03c379766e7ac32c},
doi = {10.1109/AVSS.2006.83},
isbn = {0-7695-2688-8 978-0-7695-2688-1},
year = {2006},
date = {2006-01-01},
booktitle = {Proceedings - IEEE International Conference on Video and Signal Based Surveillance 2006, AVSS 2006},
address = {Sydney, NSW},
abstract = {In this paper, we propose a novel object tracking algorithm in video sequences. The method is based on object mixture matching between successive frames of the sequence by using active contours. Only the segmentation of the objects in the first frame is required for initialization. The evolution of the object contour on a current frame aims to find the maximum fidelity of the mixture likelihood for the same object between successive frames while having the best fit of the mixture parameters to the homogenous parts of the objects. To permit for a precise and robust tracking, region, boundary and shape information are coupled in the model. The method permits for tracking multi-class objects on cluttered and non-static backgrounds. We validate our approach on examples of tracking performed on real video sequences. © 2006 IEEE.},
keywords = {Boundary conditions, Color image processing, Contour measurement, Finite mixture models, Image analysis, Level sets, Object contour tracking, Pattern matching, Shape information, Video signal processing},
pubstate = {published},
tppubtype = {inproceedings}
}
In this paper, we propose a novel object tracking algorithm in video sequences. The method is based on object mixture matching between successive frames of the sequence by using active contours. Only the segmentation of the objects in the first frame is required for initialization. The evolution of the object contour on a current frame aims to find the maximum fidelity of the mixture likelihood for the same object between successive frames while having the best fit of the mixture parameters to the homogenous parts of the objects. To permit for a precise and robust tracking, region, boundary and shape information are coupled in the model. The method permits for tracking multi-class objects on cluttered and non-static backgrounds. We validate our approach on examples of tracking performed on real video sequences. © 2006 IEEE.