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Beyond actions: Discriminative models for contextual group activities

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Thesis type
((Thesis)) M.Sc.
Date created
Author: Lan, Tian
In this dissertation, we go beyond recognizing individual person actions and focus on group activities instead. This motivates from the observation that human actions are rarely performed in isolation -- the contextual information of nearby humans provides useful cues for understanding the high-level activities. We propose a discriminative model for recognizing group activities. Our model jointly captures the group activity, the individual person actions, and the interactions among them. Two new types of contextual information, group-person interaction and person-person interaction, are explored in a latent variable framework. In particular, the person-person interaction is modeled in two ways: 1) the structure of the interaction is modeled as a latent variable and implicitly inferred during learning and inference. 2) The interaction is explored in feature level and a new contextual feature descriptor is introduced. Our experimental results demonstrate the benefit of using contextual information for disambiguating group activities.
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Supervisor or Senior Supervisor
Thesis advisor: Mori, Greg
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