Title

Teamwork Recognition Of Embodied Agents With Hidden Markov Models

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 cor-related 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 IEEE.

Publication Date

12-1-2007

Publication Title

ICCP 2007 Proceedings IEEE 3rd International Conference on Intelligent Computer Communication and Processing

Number of Pages

33-40

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

DOI Link

https://doi.org/10.1109/ICCP.2007.4352139

Socpus ID

47749107926 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/47749107926

This document is currently not available here.

Share

COinS