Adaboost-based algorithm for human action recognition

Handle URI:
http://hdl.handle.net/10754/626842
Title:
Adaboost-based algorithm for human action recognition
Authors:
Zerrouki, Nabil; Harrou, Fouzi; Sun, Ying ( 0000-0001-6703-4270 ) ; Houacine, Amrane
Abstract:
This paper presents a computer vision-based methodology for human action recognition. First, the shape based pose features are constructed based on area ratios to identify the human silhouette in images. The proposed features are invariance to translation and scaling. Once the human body features are extracted from videos, different human actions are learned individually on the training frames of each class. Then, we apply the Adaboost algorithm for the classification process. We assessed the proposed approach using the UR Fall Detection dataset. In this study six classes of activities are considered namely: walking, standing, bending, lying, squatting, and sitting. Results demonstrate the efficiency of the proposed methodology.
KAUST Department:
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division; Applied Mathematics and Computational Science Program
Citation:
Zerrouki N, Harrou F, Sun Y, Houacine A (2017) Adaboost-based algorithm for human action recognition. 2017 IEEE 15th International Conference on Industrial Informatics (INDIN). Available: http://dx.doi.org/10.1109/INDIN.2017.8104769.
Publisher:
IEEE
Journal:
2017 IEEE 15th International Conference on Industrial Informatics (INDIN)
Issue Date:
28-Nov-2017
DOI:
10.1109/INDIN.2017.8104769
Type:
Conference Paper
Additional Links:
http://ieeexplore.ieee.org/document/8104769/
Appears in Collections:
Conference Papers; Applied Mathematics and Computational Science Program; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

Full metadata record

DC FieldValue Language
dc.contributor.authorZerrouki, Nabilen
dc.contributor.authorHarrou, Fouzien
dc.contributor.authorSun, Yingen
dc.contributor.authorHouacine, Amraneen
dc.date.accessioned2018-01-21T11:13:23Z-
dc.date.available2018-01-21T11:13:23Z-
dc.date.issued2017-11-28en
dc.identifier.citationZerrouki N, Harrou F, Sun Y, Houacine A (2017) Adaboost-based algorithm for human action recognition. 2017 IEEE 15th International Conference on Industrial Informatics (INDIN). Available: http://dx.doi.org/10.1109/INDIN.2017.8104769.en
dc.identifier.doi10.1109/INDIN.2017.8104769en
dc.identifier.urihttp://hdl.handle.net/10754/626842-
dc.description.abstractThis paper presents a computer vision-based methodology for human action recognition. First, the shape based pose features are constructed based on area ratios to identify the human silhouette in images. The proposed features are invariance to translation and scaling. Once the human body features are extracted from videos, different human actions are learned individually on the training frames of each class. Then, we apply the Adaboost algorithm for the classification process. We assessed the proposed approach using the UR Fall Detection dataset. In this study six classes of activities are considered namely: walking, standing, bending, lying, squatting, and sitting. Results demonstrate the efficiency of the proposed methodology.en
dc.publisherIEEEen
dc.relation.urlhttp://ieeexplore.ieee.org/document/8104769/en
dc.rights(c) 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.en
dc.titleAdaboost-based algorithm for human action recognitionen
dc.typeConference Paperen
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Divisionen
dc.contributor.departmentApplied Mathematics and Computational Science Programen
dc.identifier.journal2017 IEEE 15th International Conference on Industrial Informatics (INDIN)en
dc.eprint.versionPost-printen
dc.contributor.institutionUniversity of Sciences and Technology Houari Boum├ędienne, Algeria, LCPTS, Faculty of Electronics and Computer Scienceen
kaust.authorHarrou, Fouzien
kaust.authorSun, Yingen
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