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dc.contributor.authorZerrouki, Nabil
dc.contributor.authorHarrou, Fouzi
dc.contributor.authorSun, Ying
dc.contributor.authorHouacine, Amrane
dc.date.accessioned2018-01-21T11:13:23Z
dc.date.available2018-01-21T11:13:23Z
dc.date.issued2017-11-28
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.
dc.identifier.doi10.1109/INDIN.2017.8104769
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.
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.relation.urlhttp://ieeexplore.ieee.org/document/8104769/
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.
dc.titleAdaboost-based algorithm for human action recognition
dc.typeConference Paper
dc.contributor.departmentApplied Mathematics and Computational Science Program
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentStatistics Program
dc.identifier.journal2017 IEEE 15th International Conference on Industrial Informatics (INDIN)
dc.eprint.versionPost-print
dc.contributor.institutionUniversity of Sciences and Technology Houari Boumédienne, Algeria, LCPTS, Faculty of Electronics and Computer Science
kaust.personHarrou, Fouzi
kaust.personSun, Ying
refterms.dateFOA2018-06-14T05:25:23Z
dc.date.published-online2017-11-28
dc.date.published-print2017-07


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