Multi-signal gesture recognition using body and hand poses
Name
710993246-MIT.pdf
Description
Full printable version
Size
16.78 MB
Format
Adobe PDF
Checksum (MD5)
23cd6624464968c0c64c38b85d1ae9b9
Author(s)
Song, Yale
Advisor(s)
Randall Davis.
Date Issued
2010
Publisher
Massachusetts Institute of Technology
Abstract
We present a vision-based multi-signal gesture recognition system that integrates information from body and hand poses. Unlike previous approaches to gesture recognition, which concentrated mainly on making it a signal signal, our system allows a richer gesture vocabulary and more natural human-computer interaction. The system consists of three parts: 3D body pose estimation, hand pose classification, and gesture recognition. 3D body pose estimation is performed following a generative model-based approach, using a particle filtering estimation framework. Hand pose classification is performed by extracting Histogram of Oriented Gradients features and using a multi-class Support Vector Machine classifier. Finally, gesture recognition is performed using a novel statistical inference framework that we developed for multi-signal pattern recognition, extending previous work on a discriminative hidden-state graphical model (HCRF) to consider multi-signal input data, which we refer to Multi Information-Channel Hidden Conditional Random Fields (MIC-HCRFs). One advantage of MIC-HCRF is that it allows us to capture complex dependencies of multiple information channels more precisely than conventional approaches to the task. Our system was evaluated on the scenario of an aircraft carrier flight deck environment, where humans interact with unmanned vehicles using existing body and hand gesture vocabulary. When tested on 10 gestures recorded from 20 participants, the average recognition accuracy of our system was 88.41%.
Description
Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2010.
Cataloged from PDF version of thesis.
Includes bibliographical references (p. 147-154).
Subjects
Electrical Engineering and Computer Science.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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