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Framework for recognizing multi-agent action from visual evidence

  • Stephen S. Intille
  • , Aaron F. Bobick

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

A probabilistic framework for representing and visually recognizing complex multi-agent action is presented. Motivated by work in model-based object recognition and designed for the recognition of action from visual evidence, the representation has three components: (1) temporal structure descriptions representing the temporal relationships between agent goals, (2) belief networks for probabilistically representing and recognizing individual agent goals from visual evidence, and (3) belief networks automatically generated from the temporal structure descriptions that support the recognition of the complex action. We describe our current work on recognizing American football plays from noisy trajectory data.

Original languageEnglish
Title of host publicationProceedings of the National Conference on Artificial Intelligence
PublisherAAAI
Pages518-525
Number of pages8
ISBN (Print)0262511061
StatePublished - 1999
EventProceedings of the 1999 16th National Conference on Artificial Intelligence (AAAI-99), 11th Innovative Applications of Artificial Intelligence Conference (IAAI-99) - Orlando, FL, USA
Duration: Jul 18 1999Jul 22 1999

Publication series

NameProceedings of the National Conference on Artificial Intelligence

Conference

ConferenceProceedings of the 1999 16th National Conference on Artificial Intelligence (AAAI-99), 11th Innovative Applications of Artificial Intelligence Conference (IAAI-99)
CityOrlando, FL, USA
Period07/18/9907/22/99

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