• DocumentCode
    1700823
  • Title

    Action Recognition from Experience

  • Author

    Tu, Peter ; Sebastian, Thomas ; Gao, Dashan

  • fYear
    2012
  • Firstpage
    124
  • Lastpage
    129
  • Abstract
    A reinforcement learning model, which allows for an agent to interact with a simulated 3D learning environment under the initial guidance of an all knowing oracle is proposed. Methods are presented that allow the agent to learn how to perform a set of task oriented actions. It is then hypothesized that the ability to recognize an action may in fact be a byproduct of learning how to perform an action. Evidence supporting this conjecture is presented using both simulated and real world imagery.
  • Keywords
    image motion analysis; image recognition; interactive systems; learning (artificial intelligence); action recognition; agent interaction; real-world imagery; reinforcement learning model; simulated 3D learning environment; simulated imagery; task-oriented actions; Avatars; Current measurement; Image recognition; Joints; Mathematical model; Springs; Vectors;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Advanced Video and Signal-Based Surveillance (AVSS), 2012 IEEE Ninth International Conference on
  • Conference_Location
    Beijing
  • Print_ISBN
    978-1-4673-2499-1
  • Type

    conf

  • DOI
    10.1109/AVSS.2012.85
  • Filename
    6327996