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

Teamwork recognition of embodied agents with hidden Markov models


Cite as:

L. J. Luotsinen, H. Fernlund, and L. Bölöni. Teamwork recognition of embodied agents with hidden Markov models. In Proceedings of the IEEE 3rd International Conference on Intelligent Computer Communication and Processing, pp. 33–40, September 2007.

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Abstract:

Recognizing and annotating the occurrence of team actions in observations of embodied agents has applications in surveillance or 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 acquiredfrom 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 ofthe recognition engine is sufficiently fast to allow real time annotation Of incoming observations.

BibTeX:

@inproceedings{Luotsinen-2007b-ICCP,
   author = "L. J. Luotsinen and H. Fernlund and L. B{\"o}l{\"o}ni",
   title = "Teamwork recognition of embodied agents with hidden Markov models",
   booktitle = "Proceedings of the IEEE 3rd International Conference on Intelligent Computer Communication and Processing",
   pages ="33-40",
   year = "2007",
   month = "September",
   abstract = {
   Recognizing and annotating the occurrence of team actions in observations of
   embodied agents has applications in surveillance or 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
   acquiredfrom 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
   ofthe recognition engine is sufficiently fast to allow real time annotation
   Of incoming observations.
   },
}

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