Boston University Libraries OpenBU
    JavaScript is disabled for your browser. Some features of this site may not work without it.
    View Item 
    •   OpenBU
    • Theses & Dissertations
    • Boston University Theses & Dissertations
    • View Item
    •   OpenBU
    • Theses & Dissertations
    • Boston University Theses & Dissertations
    • View Item

    Learning space-time structures for action recognition and localization

    Thumbnail
    License
    Attribution-NonCommercial-NoDerivatives 4.0 International
    Date Issued
    2016
    Author(s)
    Ma, Shugao
    Share to FacebookShare to TwitterShare by Email
    Export Citation
    Download to BibTex
    Download to EndNote/RefMan (RIS)
    Metadata
    Show full item record
    Permanent Link
    https://hdl.handle.net/2144/17720
    Abstract
    In this thesis the problem of automatic human action recognition and localization in videos is studied. In this problem, our goal is to recognize the category of the human action that is happening in the video, and also to localize the action in space and/or time. This problem is challenging due to the complexity of the human actions, the large intra-class variations and the distraction of backgrounds. Human actions are inherently structured patterns of body movements. However, past works are inadequate in learning the space-time structures in human actions and exploring them for better recognition and localization. In this thesis new methods are proposed that exploit such space-time structures for effective human action recognition and localization in videos, including sports videos, YouTube videos, TV programs and movies. A new local space-time video representation, the hierarchical Space-Time Segments, is first proposed. Using this new video representation, ensembles of hierarchical spatio-temporal trees, discovered directly from the training videos, are constructed to model the hierarchical, spatial and temporal structures of human actions. This proposed approach achieves promising performances in action recognition and localization on challenging benchmark datasets. Moreover, the discovered trees show good cross-dataset generalizability: trees learned on one dataset can be used to recognize and localize similar actions in another dataset. To handle large scale data, a deep model is explored that learns temporal progression of the actions using Long Short Term Memory (LSTM), which is a type of Recurrent Neural Network (RNN). Two novel ranking losses are proposed to train the model to better capture the temporal structures of actions for accurate action recognition and temporal localization. This model achieves state-of-art performance on a large scale video dataset. A deep model usually employs a Convolutional Neural Network (CNN) to learn visual features from video frames. The problem of utilizing web action images for training a Convolutional Neural Network (CNN) is also studied: training CNN typically requires a large number of training videos, but the findings of this study show that web action images can be utilized as additional training data to significantly reduce the burden of video training data collection.
    Rights
    Attribution-NonCommercial-NoDerivatives 4.0 International
    Collections
    • Boston University Theses & Dissertations [6950]


    Boston University
    Contact Us | Send Feedback | Help
     

     

    Browse

    All of OpenBUCommunities & CollectionsIssue DateAuthorsTitlesSubjectsThis CollectionIssue DateAuthorsTitlesSubjects

    Deposit Materials

    LoginNon-BU Registration

    Statistics

    Most Popular ItemsStatistics by CountryMost Popular Authors

    Boston University
    Contact Us | Send Feedback | Help