Title

Automatic Annotation Of Team Actions In Observations Of Embodied Agents

Keywords

Multi-agent behavior modeling; Teamwork recognition

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. © 2007 IFAAMAS.

Publication Date

12-1-2007

Publication Title

Proceedings of the International Conference on Autonomous Agents

Number of Pages

32-34

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

DOI Link

https://doi.org/10.1145/1329125.1329137

Socpus ID

60349091021 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/60349091021

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