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    Exploiting Human Actions and Object Context for Recognition Tasks

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    Date
    1999
    Author
    Moore, Darnell Janssen
    Essa, Irfan A.
    Hayes, M. H. (Monson H.)
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    Abstract
    Our goal is to exploit human motion and object context to perform action recognition and object classification. Towards this end, we introduce a framework for recognizing actions and objects by measuring image-, object- and action-based information from video. Hidden Markov models are combined with object context to classify hand actions, which are aggregated by a Bayesian classifier to summarize activities. We also use Bayesian methods to differentiate the class of unknown objects by evaluating detected actions along with low-level, extracted object features. Our approach is appropriate for locating and classifying objects under a variety of conditions including full occlusion. We show experiments where both familiar and previously unseen objects are recognized using action and context information.
    URI
    http://hdl.handle.net/1853/3377
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