Title:
Categorizing Turn-Taking Interactions
Categorizing Turn-Taking Interactions
Author(s)
Prabhakar, Karthir
Rehg, James M.
Rehg, James M.
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Abstract
We address the problem of categorizing turn-taking interactions between individuals. Social interactions are characterized by turn-taking and arise
frequently in real-world videos. Our approach is based on the use of temporal
causal analysis to decompose a space-time visual word representation of video
into co-occuring independent segments, called causal sets
[1]. These causal sets
then serves the input to a multiple instance learning framework to categorize turn-
taking interactions. We introduce a new turn-taking interactions dataset consisting of social games and sports rallies. We demonstrate that our formulation of
multiple instance learning (QP-MISVM) is better able to leverage the repetitive
structure in turn-taking interactions and demonstrates superior performance relative to a conventional bag of words model.
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Date Issued
2012-10
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