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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