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    Development of Human Pose Analyzing Algorithms for the Determination of Construction Productivity in Real-time

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    umi-ku-2739_1.pdf (1.739Mb)
    Issue Date
    2008-10-06
    Author
    Peddi, Abhinav
    Publisher
    University of Kansas
    Format
    110 pages
    Type
    Thesis
    Degree Level
    M.S.
    Discipline
    Electrical Engineering & Computer Science
    Rights
    This item is protected by copyright and unless otherwise specified the copyright of this thesis/dissertation is held by the author.
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    Abstract
    To enhance the capability of rapid construction, an automated on-site productivity measurement system is developed. Employing the concepts of Computer Vision and Artificial Intelligence, the developed system wirelessly acquires a sequence of images of construction activities. It first processes these images in real-time to generate human poses that are associated with construction activities at a project site. The human poses are classified into three categories as effective work, ineffective work, and contributory work. Then, a built-in neural network determines the working status of a worker by comparing in-coming images to the developed human poses. The labor productivity is determined from the comparison statistics. This system has been tested for accuracy on a bridge construction project. The results of our analysis were accurate as compared to the results produced by the traditional productivity measurement method. This research project made several major contributions to the advancement of construction industry. First, it applied advanced image processing techniques for analyzing construction operations. Second, the results of this research project made it possible to automatically determine construction productivity in real-time. Thus, an instant feedback to the construction crew was possible. As a result, the capability of rapid construction was improved using the developed technology.
    URI
    http://hdl.handle.net/1808/4283
    Collections
    • Engineering Dissertations and Theses [1055]
    • Theses [3906]

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    785-864-8983

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    Contact KU ScholarWorks
    785-864-8983
    KU Libraries
    1425 Jayhawk Blvd
    Lawrence, KS 66045
    785-864-8983

    KU Libraries
    1425 Jayhawk Blvd
    Lawrence, KS 66045
    Image Credits
     

     

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