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    Vision-based Human Action Classification Using Adaptive Boosting Algorithm

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    Type
    Article
    Authors
    Zerrouki, Nabil
    Harrou, Fouzi cc
    Sun, Ying cc
    Houacine, Amrane
    KAUST Department
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Statistics Program
    KAUST Grant Number
    OSR2015-CRG4-2582
    Date
    2018-05-07
    Online Publication Date
    2018-05-07
    Print Publication Date
    2018-06-15
    Permanent link to this record
    http://hdl.handle.net/10754/627907
    
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    Abstract
    Precise recognition of human action is a key enabler for the development of many applications including autonomous robots for medical diagnosis and surveillance of elderly people in home environment. This paper addresses the human action recognition based on variation in body shape. Specifically, we divide the human body into five partitions that correspond to five partial occupancy areas. For each frame, we calculated area ratios and used them as input data for recognition stage. Here, we consider six classes of activities namely: walking, standing, bending, lying, squatting, and sitting. In this paper, we proposed an efficient human action recognition scheme, which takes advantages of superior discrimination capacity of AdaBoost algorithm. We validated the effectiveness of this approach by using experimental data from two publicly available databases fall detection databases from the University of Rzeszow’s and the Universidad de Málaga fall detection datasets. We provided comparisons of the proposed approach with state-of-the-art classifiers based on the neural network, K-nearest neighbor, support vector machine and naïve Bayes and showed that we achieve better results in discriminating human gestures.
    Citation
    Zerrouki N, Harrou F, Sun Y, Houacine A (2018) Vision-based Human Action Classification Using Adaptive Boosting Algorithm. IEEE Sensors Journal: 1–1. Available: http://dx.doi.org/10.1109/JSEN.2018.2830743.
    Sponsors
    This publication is based upon work supported by the King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research (OSR) under Award No: OSR2015-CRG4-2582. We are grateful to the two referees, the Associate Editor, and the Editor-in-Chief for their comments.
    Publisher
    Institute of Electrical and Electronics Engineers (IEEE)
    Journal
    IEEE Sensors Journal
    DOI
    10.1109/JSEN.2018.2830743
    Additional Links
    https://ieeexplore.ieee.org/document/8355489
    ae974a485f413a2113503eed53cd6c53
    10.1109/JSEN.2018.2830743
    Scopus Count
    Collections
    Articles; Statistics Program; Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

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