Slide
Centre Interdisciplinaire
de Recherche et d’Innovation
en Cybersécurité et Société
de Recherche et d’Innovation
en Cybersécurité et Société
1.
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.
2.
Allili, M. S.; Ziou, D.
Active contours for video object tracking using region, boundary and shape information Journal Article
In: Signal, Image and Video Processing, vol. 1, no. 2, pp. 101–117, 2007, ISSN: 18631711 (ISSN).
Abstract | Links | BibTeX | Tags: Color and infrared, KL-distance, Level sets, Mixture models, Region/Boundary/Shape information, Tracking
@article{allili_active_2007,
title = {Active contours for video object tracking using region, boundary and shape information},
author = {M. S. Allili and D. Ziou},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-34250211807&doi=10.1007%2fs11760-007-0021-8&partnerID=40&md5=a92c80484411a508c57587d27b7d357b},
doi = {10.1007/s11760-007-0021-8},
issn = {18631711 (ISSN)},
year = {2007},
date = {2007-01-01},
journal = {Signal, Image and Video Processing},
volume = {1},
number = {2},
pages = {101–117},
abstract = {In this paper, we propose a robust model for tracking in video sequences with non-static backgrounds. The object boundaries are tracked on each frame of the sequence by minimizing an energy functional that combines region, boundary and shape information. The region information is formulated by minimizing the symmetric Kullback-Leibler (KL) distance between the local and global statistics of the objects versus the background. The boundary information is formulated using a color and texture edge map of the video frames. The shape information is calculated adaptively to the dynamic of the moving objects and permits tracking that is robust to background distractions and occlusions. Minimization of the energy functional is implemented using the level set method. We show the effectiveness of the approach for object tracking in color, infrared (IR), and fused color-infrared sequences. © Springer-Verlag London Limited 2007.},
keywords = {Color and infrared, KL-distance, Level sets, Mixture models, Region/Boundary/Shape information, Tracking},
pubstate = {published},
tppubtype = {article}
}
In this paper, we propose a robust model for tracking in video sequences with non-static backgrounds. The object boundaries are tracked on each frame of the sequence by minimizing an energy functional that combines region, boundary and shape information. The region information is formulated by minimizing the symmetric Kullback-Leibler (KL) distance between the local and global statistics of the objects versus the background. The boundary information is formulated using a color and texture edge map of the video frames. The shape information is calculated adaptively to the dynamic of the moving objects and permits tracking that is robust to background distractions and occlusions. Minimization of the energy functional is implemented using the level set method. We show the effectiveness of the approach for object tracking in color, infrared (IR), and fused color-infrared sequences. © Springer-Verlag London Limited 2007.
3.
Allili, M. S.; Ziou, D.
Globally adaptive region information for automatic color-texture image segmentation Journal Article
In: Pattern Recognition Letters, vol. 28, no. 15, pp. 1946–1956, 2007, ISSN: 01678655.
Abstract | Links | BibTeX | Tags: Algorithms, Automatic segmentation, Boundary information, Color image processing, Color texture image segmentation, Contour measurement, Image analysis, Image segmentation, Level sets, Polarity, Textures
@article{allili_globally_2007,
title = {Globally adaptive region information for automatic color-texture image segmentation},
author = {M. S. Allili and D. Ziou},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-34548675168&doi=10.1016%2fj.patrec.2007.05.002&partnerID=40&md5=e1338223b9cb99afc35dfbfbf7859b72},
doi = {10.1016/j.patrec.2007.05.002},
issn = {01678655},
year = {2007},
date = {2007-01-01},
journal = {Pattern Recognition Letters},
volume = {28},
number = {15},
pages = {1946–1956},
abstract = {In this paper, we propose an automatic segmentation of color-texture images with arbitrary numbers of regions. The approach combines region and boundary information and uses active contours to build a partition of the image. The segmentation algorithm is initialized automatically by using homogeneous region seeds on the image domain. The partition of the image is formed by evolving the region contours and adaptively updating the region information formulated using a mixture of pdfs. We show the performance of the proposed method on examples of color-texture image segmentation, with comparison to two state-of-the-art methods. © 2007 Elsevier B.V. All rights reserved.},
keywords = {Algorithms, Automatic segmentation, Boundary information, Color image processing, Color texture image segmentation, Contour measurement, Image analysis, Image segmentation, Level sets, Polarity, Textures},
pubstate = {published},
tppubtype = {article}
}
In this paper, we propose an automatic segmentation of color-texture images with arbitrary numbers of regions. The approach combines region and boundary information and uses active contours to build a partition of the image. The segmentation algorithm is initialized automatically by using homogeneous region seeds on the image domain. The partition of the image is formed by evolving the region contours and adaptively updating the region information formulated using a mixture of pdfs. We show the performance of the proposed method on examples of color-texture image segmentation, with comparison to two state-of-the-art methods. © 2007 Elsevier B.V. All rights reserved.
4.
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{alliliAdaptiveAppearanceModel2007,
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.
5.
Allili, M. S.; Ziou, D.
Active contours for video object tracking using region, boundary and shape information Journal Article
In: Signal, Image and Video Processing, vol. 1, no. 2, pp. 101–117, 2007, ISSN: 18631711 (ISSN).
Abstract | Links | BibTeX | Tags: Color and infrared, KL-distance, Level sets, Mixture models, Region/Boundary/Shape information, Tracking
@article{alliliActiveContoursVideo2007,
title = {Active contours for video object tracking using region, boundary and shape information},
author = {M. S. Allili and D. Ziou},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-34250211807&doi=10.1007%2fs11760-007-0021-8&partnerID=40&md5=a92c80484411a508c57587d27b7d357b},
doi = {10.1007/s11760-007-0021-8},
issn = {18631711 (ISSN)},
year = {2007},
date = {2007-01-01},
journal = {Signal, Image and Video Processing},
volume = {1},
number = {2},
pages = {101–117},
abstract = {In this paper, we propose a robust model for tracking in video sequences with non-static backgrounds. The object boundaries are tracked on each frame of the sequence by minimizing an energy functional that combines region, boundary and shape information. The region information is formulated by minimizing the symmetric Kullback-Leibler (KL) distance between the local and global statistics of the objects versus the background. The boundary information is formulated using a color and texture edge map of the video frames. The shape information is calculated adaptively to the dynamic of the moving objects and permits tracking that is robust to background distractions and occlusions. Minimization of the energy functional is implemented using the level set method. We show the effectiveness of the approach for object tracking in color, infrared (IR), and fused color-infrared sequences. © Springer-Verlag London Limited 2007.},
keywords = {Color and infrared, KL-distance, Level sets, Mixture models, Region/Boundary/Shape information, Tracking},
pubstate = {published},
tppubtype = {article}
}
In this paper, we propose a robust model for tracking in video sequences with non-static backgrounds. The object boundaries are tracked on each frame of the sequence by minimizing an energy functional that combines region, boundary and shape information. The region information is formulated by minimizing the symmetric Kullback-Leibler (KL) distance between the local and global statistics of the objects versus the background. The boundary information is formulated using a color and texture edge map of the video frames. The shape information is calculated adaptively to the dynamic of the moving objects and permits tracking that is robust to background distractions and occlusions. Minimization of the energy functional is implemented using the level set method. We show the effectiveness of the approach for object tracking in color, infrared (IR), and fused color-infrared sequences. © Springer-Verlag London Limited 2007.
6.
Allili, M. S.; Ziou, D.
Globally adaptive region information for automatic color-texture image segmentation Journal Article
In: Pattern Recognition Letters, vol. 28, no. 15, pp. 1946–1956, 2007, ISSN: 01678655.
Abstract | Links | BibTeX | Tags: Algorithms, Automatic segmentation, Boundary information, Color image processing, Color texture image segmentation, Contour measurement, Image analysis, Image segmentation, Level sets, Polarity, Textures
@article{alliliGloballyAdaptiveRegion2007,
title = {Globally adaptive region information for automatic color-texture image segmentation},
author = {M. S. Allili and D. Ziou},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-34548675168&doi=10.1016%2fj.patrec.2007.05.002&partnerID=40&md5=e1338223b9cb99afc35dfbfbf7859b72},
doi = {10.1016/j.patrec.2007.05.002},
issn = {01678655},
year = {2007},
date = {2007-01-01},
journal = {Pattern Recognition Letters},
volume = {28},
number = {15},
pages = {1946–1956},
abstract = {In this paper, we propose an automatic segmentation of color-texture images with arbitrary numbers of regions. The approach combines region and boundary information and uses active contours to build a partition of the image. The segmentation algorithm is initialized automatically by using homogeneous region seeds on the image domain. The partition of the image is formed by evolving the region contours and adaptively updating the region information formulated using a mixture of pdfs. We show the performance of the proposed method on examples of color-texture image segmentation, with comparison to two state-of-the-art methods. © 2007 Elsevier B.V. All rights reserved.},
keywords = {Algorithms, Automatic segmentation, Boundary information, Color image processing, Color texture image segmentation, Contour measurement, Image analysis, Image segmentation, Level sets, Polarity, Textures},
pubstate = {published},
tppubtype = {article}
}
In this paper, we propose an automatic segmentation of color-texture images with arbitrary numbers of regions. The approach combines region and boundary information and uses active contours to build a partition of the image. The segmentation algorithm is initialized automatically by using homogeneous region seeds on the image domain. The partition of the image is formed by evolving the region contours and adaptively updating the region information formulated using a mixture of pdfs. We show the performance of the proposed method on examples of color-texture image segmentation, with comparison to two state-of-the-art methods. © 2007 Elsevier B.V. All rights reserved.
7.
Allili, M. S.; Ziou, D.
A robust video object tracking by using active contours Proceedings Article
In: 2006 Conference on Computer Vision and Pattern Recognition Workshops, pp. 135, IEEE Computer Society, New York, NY, 2006, ISBN: 0769526462 (ISBN); 978-076952646-1 (ISBN), (Journal Abbreviation: Conf. Comput. Vision Pattern Recog. Workshops).
Abstract | Links | BibTeX | Tags: Boundary, Boundary localization, Color, Feature distribution, Image processing, Image segmentation, Kullback-Leibler distance, Level sets, Mathematical models, Mixture of pdfs, Object recognition, Object Tracking, Texture, Tracking (position), Variational techniques, Video object tracking
@inproceedings{allili_robust_2006,
title = {A robust video object tracking by using active contours},
author = {M. S. Allili and D. Ziou},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-33845513941&doi=10.1109%2fCVPRW.2006.20&partnerID=40&md5=64ff2be5c45a6c206420bf6eb5589bca},
doi = {10.1109/CVPRW.2006.20},
isbn = {0769526462 (ISBN); 978-076952646-1 (ISBN)},
year = {2006},
date = {2006-01-01},
booktitle = {2006 Conference on Computer Vision and Pattern Recognition Workshops},
volume = {2006},
pages = {135},
publisher = {IEEE Computer Society},
address = {New York, NY},
abstract = {In this paper, we propose a novel object tracking algorithm in video sequences. The formulation of our tracking model is based on variational calculus, where region and boundary information cooperate for object boundary localization by using active contours. In the approach, only the segmentation of the objects in the first frame is required for initialization. The evolution of the object contours on a current frame aims to find the boundary of the objects by minimizing the Kullback-Leibler distance of the region feature s distribution in the vicinity of the contour to the objects versus the background respectively. We show the effectiveness of the approach on examples of object tracking performed on real video sequences. © 2006 IEEE.},
note = {Journal Abbreviation: Conf. Comput. Vision Pattern Recog. Workshops},
keywords = {Boundary, Boundary localization, Color, Feature distribution, Image processing, Image segmentation, Kullback-Leibler distance, Level sets, Mathematical models, Mixture of pdfs, Object recognition, Object Tracking, Texture, Tracking (position), Variational techniques, Video object tracking},
pubstate = {published},
tppubtype = {inproceedings}
}
In this paper, we propose a novel object tracking algorithm in video sequences. The formulation of our tracking model is based on variational calculus, where region and boundary information cooperate for object boundary localization by using active contours. In the approach, only the segmentation of the objects in the first frame is required for initialization. The evolution of the object contours on a current frame aims to find the boundary of the objects by minimizing the Kullback-Leibler distance of the region feature s distribution in the vicinity of the contour to the objects versus the background respectively. We show the effectiveness of the approach on examples of object tracking performed on real video sequences. © 2006 IEEE.
8.
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.
9.
Allili, M. S.; Ziou, D.
A robust video object tracking by using active contours Proceedings Article
In: 2006 Conference on Computer Vision and Pattern Recognition Workshops, pp. 135, IEEE Computer Society, New York, NY, 2006, ISBN: 0769526462 (ISBN); 978-076952646-1 (ISBN), (Journal Abbreviation: Conf. Comput. Vision Pattern Recog. Workshops).
Abstract | Links | BibTeX | Tags: Boundary, Boundary localization, Color, Feature distribution, Image processing, Image segmentation, Kullback-Leibler distance, Level sets, Mathematical models, Mixture of pdfs, Object recognition, Object Tracking, Texture, Tracking (position), Variational techniques, Video object tracking
@inproceedings{alliliRobustVideoObject2006,
title = {A robust video object tracking by using active contours},
author = {M. S. Allili and D. Ziou},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-33845513941&doi=10.1109%2fCVPRW.2006.20&partnerID=40&md5=64ff2be5c45a6c206420bf6eb5589bca},
doi = {10.1109/CVPRW.2006.20},
isbn = {0769526462 (ISBN); 978-076952646-1 (ISBN)},
year = {2006},
date = {2006-01-01},
booktitle = {2006 Conference on Computer Vision and Pattern Recognition Workshops},
volume = {2006},
pages = {135},
publisher = {IEEE Computer Society},
address = {New York, NY},
abstract = {In this paper, we propose a novel object tracking algorithm in video sequences. The formulation of our tracking model is based on variational calculus, where region and boundary information cooperate for object boundary localization by using active contours. In the approach, only the segmentation of the objects in the first frame is required for initialization. The evolution of the object contours on a current frame aims to find the boundary of the objects by minimizing the Kullback-Leibler distance of the region feature s distribution in the vicinity of the contour to the objects versus the background respectively. We show the effectiveness of the approach on examples of object tracking performed on real video sequences. © 2006 IEEE.},
note = {Journal Abbreviation: Conf. Comput. Vision Pattern Recog. Workshops},
keywords = {Boundary, Boundary localization, Color, Feature distribution, Image processing, Image segmentation, Kullback-Leibler distance, Level sets, Mathematical models, Mixture of pdfs, Object recognition, Object Tracking, Texture, Tracking (position), Variational techniques, Video object tracking},
pubstate = {published},
tppubtype = {inproceedings}
}
In this paper, we propose a novel object tracking algorithm in video sequences. The formulation of our tracking model is based on variational calculus, where region and boundary information cooperate for object boundary localization by using active contours. In the approach, only the segmentation of the objects in the first frame is required for initialization. The evolution of the object contours on a current frame aims to find the boundary of the objects by minimizing the Kullback-Leibler distance of the region feature s distribution in the vicinity of the contour to the objects versus the background respectively. We show the effectiveness of the approach on examples of object tracking performed on real video sequences. © 2006 IEEE.
10.
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{alliliObjectContourTracking2006,
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.