L. J. Luotsinen, H. Fernlund, and L. Bölöni

Automatic annotation of team actions in observations of embodied agents


Cite as:

L. J. Luotsinen, H. Fernlund, and L. Bölöni. Automatic annotation of team actions in observations of embodied agents. In The Sixth Intl. Joint Conf. on Autonomous Agents and Multi-Agent Systems (AAMAS 07), pp. 32–34, 2007.

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Abstract:

Recognizing and annotating the occurrence of team actions in observations of embodied agents has applications in surveillance and in training of military or sport teams. We describe the team actions through a spatio-temporal correlated pattern of movement, which can be modeled by a Hidden Markov Model. The hand-crafting of these models is a difficult task of knowledge engineering, even in application domains where explicit, natural language descriptions of the team actions are available. The main contribution of this paper is an approach through which the library of HMM representations can be acquired from a small number of hand annotated, representative samples of the specific movement patterns. A series of experiments, performed on a dataset describing a real-world terrestrial warfare exercise validates our method and shows good recognition accuracy even in the presence of noisy data. The speed of the recognition engine is sufficiently fast to allow real time annotation of incoming observations.

BibTeX:

@inproceedings{Luotsinen-2007-AAMAS,
author = "L. J. Luotsinen and H. Fernlund and L. B{\"o}l{\"o}ni",
title = "Automatic annotation of team actions in observations of embodied agents",
booktitle = "The Sixth Intl. Joint Conf. on Autonomous Agents and Multi-Agent Systems (AAMAS 07)",
year = "2007",
pages = "32-34",
mynote = "Acceptance rate, short papers 47\%",
abstract = {
  Recognizing and annotating the occurrence of team actions in observations
  of embodied agents has applications in surveillance and in training of
  military or sport teams. We describe the team actions through a
  spatio-temporal correlated pattern of movement, which can be modeled by a
  Hidden Markov Model. The hand-crafting of these models is a difficult
  task of knowledge engineering, even in application domains where
  explicit, natural language descriptions of the team actions are
  available. The main contribution of this paper is an approach through
  which the library of HMM representations can be acquired from a small
  number of hand annotated, representative samples of the specific movement
  patterns. A series of experiments, performed on a dataset describing a
  real-world terrestrial warfare exercise validates our method and shows
  good recognition accuracy even in the presence of noisy data. The speed
  of the recognition engine is sufficiently fast to allow real time
  annotation of incoming observations.
 }
}

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