Gesture spotting and recognition using salience detection and concatenated hidden markov models

Author(s)
Yin, Ying; Davis, Randall
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Abstract
We developed a gesture salience based hand tracking method, and a gesture spotting and recognition method based on concatenated hidden Markov models. A 3-fold cross validation using the ChAirGest development data set with 10 users gives an F1 score of 0.907 and an accurate temporal segmentation rate (ATSR) of 0.923. The average final score is 0.9116. Compared with using the hand joint position from the Kinect SDK, using our hand tracking method gives a 3.7% absolute increase in the recognition F1 score.
Date issued
2013-12
URI
http://hdl.handle.net/1721.1/86128
Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory; Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Journal
Proceedings of the 15th ACM on International conference on multimodal interaction (ICMI '13)
Citation
Ying Yin and Randall Davis. 2013. Gesture spotting and recognition using salience detection and concatenated hidden markov models. In Proceedings of the 15th ACM on International conference on multimodal interaction (ICMI '13). ACM, New York, NY, USA, 489-494.
Version: Author's final manuscript
ISBN
9781450321297
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