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    Adaboost-based algorithm for human action recognition

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    Type
    Conference Paper
    Authors
    Zerrouki, Nabil
    Harrou, Fouzi cc
    Sun, Ying cc
    Houacine, Amrane
    KAUST Department
    Applied Mathematics and Computational Science Program
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Statistics Program
    Date
    2017-11-28
    Online Publication Date
    2017-11-28
    Print Publication Date
    2017-07
    Permanent link to this record
    http://hdl.handle.net/10754/626842
    
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    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.
    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
    Institute of Electrical and Electronics Engineers (IEEE)
    Journal
    2017 IEEE 15th International Conference on Industrial Informatics (INDIN)
    DOI
    10.1109/INDIN.2017.8104769
    Additional Links
    http://ieeexplore.ieee.org/document/8104769/
    ae974a485f413a2113503eed53cd6c53
    10.1109/INDIN.2017.8104769
    Scopus Count
    Collections
    Conference Papers; Applied Mathematics and Computational Science Program; Statistics Program; Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

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