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Categorizing Turn-Taking Interactions
Karthir Prabhakar and James M. Rehg
Center for Behavior Imaging and RIM@GT School of Interactive Computing, Georgia Institute of Technology, USAkarthir.prabhakar@cc.gatech.edu
rehg@cc.gatech.edu
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.
LNCS 7576, p. 383 ff.
lncs@springer.com
© Springer-Verlag Berlin Heidelberg 2012