Bayesian Inference of Recursive Sequences of Group Activities from Tracks

Title:
Bayesian Inference of Recursive Sequences of Group Activities from Tracks
Authors:
Brau, Ernesto; Dawson, Colin; Carrillo, Alfredo; Sidi, David; Morrison, Clayton T.
Abstract:
We present a probabilistic generative model for inferring a description of coordinated, recursively structured group activities at multiple levels of temporal granularity based on observations of individuals’ trajectories. The model accommodates: (1) hierarchically structured groups, (2) activities that are temporally and compositionally recursive, (3) component roles assigning different subactivity dynamics to subgroups of participants, and (4) a nonparametric Gaussian Process model of trajectories. We present an MCMC sampling framework for performing joint inference over recursive activity descriptions and assignment of trajectories to groups, integrating out continuous parameters. We demonstrate the model’s expressive power in several simulated and complex real-world scenarios from the VIRAT and UCLA Aerial Event video data sets.
Citation:
Brau, E., C.R. Dawson, A. Carrillo, et al. “Bayesian Inference of Recursive Sequences of Group Activities from Tracks.” In Proceedings of the 30th AAAI Conference on Artificial Intelligence (AAAI-16), 1129-1137. Palo Alto, CA: AAAI Press, 2016.
Publisher:
AAAI Press
DATE ISSUED:
2016
Department:
Mathematics
Type:
Article
Additional Links:
http://www.aaai.org/ocs/index.php/AAAI/AAAI16/paper/download/12513/11713
Notes:
AAAI-16 was held from February 12–17, 2016, Phoenix, Arizona.
PERMANENT LINK:
http://hdl.handle.net/11282/620461

Full metadata record

DC FieldValue Language
dc.contributor.authorBrau, Ernestoen
dc.contributor.authorDawson, Colinen
dc.contributor.authorCarrillo, Alfredoen
dc.contributor.authorSidi, Daviden
dc.contributor.authorMorrison, Clayton T.en
dc.date.accessioned2017-05-24T12:58:47Z-
dc.date.available2017-05-24T12:58:47Z-
dc.date.issued2016-
dc.identifier.citationBrau, E., C.R. Dawson, A. Carrillo, et al. “Bayesian Inference of Recursive Sequences of Group Activities from Tracks.” In Proceedings of the 30th AAAI Conference on Artificial Intelligence (AAAI-16), 1129-1137. Palo Alto, CA: AAAI Press, 2016.en
dc.identifier.urihttp://hdl.handle.net/11282/620461-
dc.description.abstractWe present a probabilistic generative model for inferring a description of coordinated, recursively structured group activities at multiple levels of temporal granularity based on observations of individuals’ trajectories. The model accommodates: (1) hierarchically structured groups, (2) activities that are temporally and compositionally recursive, (3) component roles assigning different subactivity dynamics to subgroups of participants, and (4) a nonparametric Gaussian Process model of trajectories. We present an MCMC sampling framework for performing joint inference over recursive activity descriptions and assignment of trajectories to groups, integrating out continuous parameters. We demonstrate the model’s expressive power in several simulated and complex real-world scenarios from the VIRAT and UCLA Aerial Event video data sets.en
dc.language.isoen_USen
dc.publisherAAAI Pressen
dc.relation.urlhttp://www.aaai.org/ocs/index.php/AAAI/AAAI16/paper/download/12513/11713en
dc.subject.departmentMathematicsen_US
dc.titleBayesian Inference of Recursive Sequences of Group Activities from Tracksen_US
dc.typeArticleen
dc.description.notesAAAI-16 was held from February 12–17, 2016, Phoenix, Arizona.en_US
dc.identifier.isbn978-1-57735-760-5 (6 vol. set)-
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