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RECOGNIZING TEAMWORK ACTIVITY IN OBSERVATIONS OF EMBODIED AGENTS

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Date Issued:
2007
Abstract/Description:
This thesis presents contributions to the theory and practice of team activity recognition. A particular focus of our work was to improve our ability to collect and label representative samples, thus making the team activity recognition more efficient. A second focus of our work is improving the robustness of the recognition process in the presence of noisy and distorted data. The main contributions of this thesis are as follows: We developed a software tool, the Teamwork Scenario Editor (TSE), for the acquisition, segmentation and labeling of teamwork data. Using the TSE we acquired a corpus of labeled team actions both from synthetic and real world sources. We developed an approach through which representations of idealized team actions can be acquired in form of Hidden Markov Models which are trained using a small set of representative examples segmented and labeled with the TSE. We developed set of team-oriented feature functions, which extract discrete features from the high-dimensional continuous data. The features were chosen such that they mimic the features used by humans when recognizing teamwork actions. We developed a technique to recognize the likely roles played by agents in teams even before the team action was recognized. Through experimental studies we show that the feature functions and role recognition module significantly increase the recognition accuracy, while allowing arbitrary shuffled inputs and noisy data.
Title: RECOGNIZING TEAMWORK ACTIVITY IN OBSERVATIONS OF EMBODIED AGENTS.
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Name(s): Luotsinen, Linus, Author
Boloni, Lotzi, Committee Chair
University of Central Florida, Degree Grantor
Type of Resource: text
Date Issued: 2007
Publisher: University of Central Florida
Language(s): English
Abstract/Description: This thesis presents contributions to the theory and practice of team activity recognition. A particular focus of our work was to improve our ability to collect and label representative samples, thus making the team activity recognition more efficient. A second focus of our work is improving the robustness of the recognition process in the presence of noisy and distorted data. The main contributions of this thesis are as follows: We developed a software tool, the Teamwork Scenario Editor (TSE), for the acquisition, segmentation and labeling of teamwork data. Using the TSE we acquired a corpus of labeled team actions both from synthetic and real world sources. We developed an approach through which representations of idealized team actions can be acquired in form of Hidden Markov Models which are trained using a small set of representative examples segmented and labeled with the TSE. We developed set of team-oriented feature functions, which extract discrete features from the high-dimensional continuous data. The features were chosen such that they mimic the features used by humans when recognizing teamwork actions. We developed a technique to recognize the likely roles played by agents in teams even before the team action was recognized. Through experimental studies we show that the feature functions and role recognition module significantly increase the recognition accuracy, while allowing arbitrary shuffled inputs and noisy data.
Identifier: CFE0001876 (IID), ucf:47409 (fedora)
Note(s): 2007-12-01
Ph.D.
Engineering and Computer Science, School of Electrical Engineering and Computer Science
Doctorate
This record was generated from author submitted information.
Subject(s): teamwork activity recognition
Persistent Link to This Record: http://purl.flvc.org/ucf/fd/CFE0001876
Restrictions on Access: public
Host Institution: UCF

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