Keywords

teamwork activity recognition

Abstract

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.

Notes

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Graduation Date

2007

Semester

Fall

Advisor

Boloni, Ladislau

Degree

Doctor of Philosophy (Ph.D.)

College

College of Engineering and Computer Science

Department

Electrical Engineering and Computer Science

Degree Program

Computer Engineering

Format

application/pdf

Identifier

CFE0001876

URL

http://purl.fcla.edu/fcla/etd/CFE0001876

Language

English

Release Date

December 2007

Length of Campus-only Access

None

Access Status

Doctoral Dissertation (Open Access)

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