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Pedrocca, P. J.; Allili, M. S.
Real-time people detection in videos using geometrical features and adaptive boosting Journal Article
In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 6753 LNCS, no. PART 1, pp. 314–324, 2011, ISSN: 03029743, (ISBN: 9783642215926 Place: Burnaby, BC).
Abstract | Links | BibTeX | Tags: Adaboost learning, Adaptive boosting, Change detection algorithms, Feature analysis, Feature extraction, Geometrical features, Geometry, Image analysis, Object recognition, Pedestrian detection, People detection, Real world videos, Signal detection, Video sequences
@article{pedrocca_real-time_2011,
title = {Real-time people detection in videos using geometrical features and adaptive boosting},
author = {P. J. Pedrocca and M. S. Allili},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-79960336661&doi=10.1007%2f978-3-642-21593-3_32&partnerID=40&md5=47ca975800e68648e02f76eba89a7457},
doi = {10.1007/978-3-642-21593-3_32},
issn = {03029743},
year = {2011},
date = {2011-01-01},
journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
volume = {6753 LNCS},
number = {PART 1},
pages = {314–324},
abstract = {In this paper, we propose a new approach for detecting people in video sequences based on geometrical features and AdaBoost learning. Unlike its predecessors, our approach uses features calculated directly from silhouettes produced by change detection algorithms. Moreover, feature analysis is done part by part for each silhouette, making our approach efficiently applicable for partially-occluded pedestrians and groups of people detection. Experiments on real-world videos showed us the performance of the proposed approach for real-time pedestrian detection. © 2011 Springer-Verlag.},
note = {ISBN: 9783642215926
Place: Burnaby, BC},
keywords = {Adaboost learning, Adaptive boosting, Change detection algorithms, Feature analysis, Feature extraction, Geometrical features, Geometry, Image analysis, Object recognition, Pedestrian detection, People detection, Real world videos, Signal detection, Video sequences},
pubstate = {published},
tppubtype = {article}
}
Allili, M. S.; Ziou, D.
Object tracking in videos using adaptive mixture models and active contours Journal Article
In: Neurocomputing, vol. 71, no. 10-12, pp. 2001–2011, 2008, ISSN: 09252312.
Abstract | Links | BibTeX | Tags: Active contours, algorithm, Algorithms, article, controlled study, Image analysis, Image processing, imaging system, Level set method, Mathematical models, motion analysis system, Object recognition, priority journal, Set theory, statistical model, Video cameras, Video sequences, videorecording, visual information
@article{allili_object_2008,
title = {Object tracking in videos using adaptive mixture models and active contours},
author = {M. S. Allili and D. Ziou},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-44649197137&doi=10.1016%2fj.neucom.2007.10.019&partnerID=40&md5=a2aef677fae1b220f68c9fd720be3fd5},
doi = {10.1016/j.neucom.2007.10.019},
issn = {09252312},
year = {2008},
date = {2008-01-01},
journal = {Neurocomputing},
volume = {71},
number = {10-12},
pages = {2001–2011},
abstract = {In this paper, we propose a novel object tracking algorithm for video sequences, based on active contours. The tracking is based on matching the object appearance model between successive frames of the sequence using active contours. We formulate the tracking as a minimization of an objective function incorporating region, boundary and shape information. Further, in order to handle variation in object appearance due to self-shadowing, changing illumination conditions and camera geometry, we propose an adaptive mixture model for the object representation. The implementation of the method is based on the level set method. We validate our approach on tracking examples using real video sequences, with comparison to two recent state-of-the-art methods. © 2008 Elsevier B.V. All rights reserved.},
keywords = {Active contours, algorithm, Algorithms, article, controlled study, Image analysis, Image processing, imaging system, Level set method, Mathematical models, motion analysis system, Object recognition, priority journal, Set theory, statistical model, Video cameras, Video sequences, videorecording, visual information},
pubstate = {published},
tppubtype = {article}
}
Allili, M. S.; Ziou, D.
Object contour tracking in videos by using adaptive mixture models and shape priors Proceedings Article
In: Proceedings of the International Symposium CompIMAGE 2006 - Computational Modelling of Objects Represented in Images: Fundamentals, Methods and Applications, pp. 47–52, Coimbra, 2007, ISBN: 978-0-415-43349-5.
Abstract | Links | BibTeX | Tags: Active contours, Best fits, Current frames, Image matching, Maximum likelihood, Mixture models, Mixtures, Multi class, Non-static backgrounds, Object contours, Object tracking algorithms, Real video sequences, Robust tracking, Shape informations, Shape priors, Video recording, Video sequences
@inproceedings{allili_object_2007-1,
title = {Object contour tracking in videos by using adaptive mixture models and shape priors},
author = {M. S. Allili and D. Ziou},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-60949085472&partnerID=40&md5=ba63e1abbabcfdd48583b41f700508ef},
isbn = {978-0-415-43349-5},
year = {2007},
date = {2007-01-01},
booktitle = {Proceedings of the International Symposium CompIMAGE 2006 - Computational Modelling of Objects Represented in Images: Fundamentals, Methods and Applications},
pages = {47–52},
address = {Coimbra},
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. © 2007 Taylor & Francis Group.},
keywords = {Active contours, Best fits, Current frames, Image matching, Maximum likelihood, Mixture models, Mixtures, Multi class, Non-static backgrounds, Object contours, Object tracking algorithms, Real video sequences, Robust tracking, Shape informations, Shape priors, Video recording, Video sequences},
pubstate = {published},
tppubtype = {inproceedings}
}
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}
}