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    Vision-based Contingency Detection

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    lee11_hri_contingency.pdf (1.424Mb)
    Date
    2011
    Author
    Lee, Jinhan
    Kiser, Jeffrey F.
    Bobick, Aaron F.
    Thomaz, Andrea L.
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    Abstract
    We present a novel method for the visual detection of a contingent response by a human to the stimulus of a robot action. Contingency is de ned as a change in an agent's be- havior within a speci c time window in direct response to a signal from another agent; detection of such responses is essential to assess the willingness and interest of a human in interacting with the robot. Using motion-based features to describe the possible contingent action, our approach as- sesses the visual self-similarity of video subsequences cap- tured before the robot exhibits its signaling behavior and statistically models the typical graph-partitioning cost of separating an arbitrary subsequence of frames from the oth- ers. After the behavioral signal, the video is similarly ana- lyzed and the cost of separating the after-signal frames from the before-signal sequences is computed; a lower than typ- ical cost indicates likely contingent reaction. We present a preliminary study in which data were captured and analyzed for algorithmic performance.
    URI
    http://hdl.handle.net/1853/38254
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    • Socially Intelligent Machines Lab (SIM) [32]
    • Socially Intelligent Machines Lab (SIM) Publications [32]

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