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Evaluation of HMM Training Algorithms for Letter Hand Gesture Recognition
Liu, Nianjun, Lovell, Brian C. and Kootsookos, Peter J. (2003). Evaluation of HMM Training Algorithms for Letter Hand Gesture Recognition. In: A. Gershman and D. Serpanos, Proceedings of the IEEE International Symposium on Signal Processing and Information Technology. IEEE International Symposium on Signal Processing and Information Technology, Darmstadt, (WA4-7). 14-17 December.
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| Name |
Description |
MIMEType |
Size |
Downloads |
paperWA4-7.pdf
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paperWA4-7.pdf |
application/pdf |
327.41KB |
1457 |
| Author(s) |
Liu, Nianjun
Lovell, Brian C.
Kootsookos, Peter J.
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| Title of paper |
Evaluation of HMM Training Algorithms for Letter Hand Gesture Recognition
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| Conference name |
IEEE International Symposium on Signal Processing and Information Technology
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| Conference location |
Darmstadt
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| Conference dates |
14-17 December
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| Proceedings title |
Proceedings of the IEEE International Symposium on Signal Processing and Information Technology
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| Editor(s) |
A. Gershman
D. Serpanos
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| Place published |
Darmstadt, Germany
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| Publisher |
The Institute of Electrical and Electronics Engineers
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| Publication date |
2003
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| Volume number |
1
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| Issue number |
1
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| Start page |
WA4
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| End page |
7
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| Total pages |
4
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| Language |
eng
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| Abstract/Summary |
The paper introduces an application using computer vision for letter hand gesture recognition. A digital camera records a video stream of hand gestures. The hand is automatically segmented, the position of the hand centroid is calculated in each frame, and a trajectory of the hand is determined. After smoothing the trajectory, a sequence of angles of motion along the trajectory is calculated and quantized to form a discrete observation sequence. Hidden Markov Models (HMMs) are used to recognize the letters. Baum Welch and Viterbi Path Counting algorithms are applied for training the HMMs. Our system recognizes all 26 letters from A to Z and the database contains 30 example videos of each letter gesture. We achieve an average recognition rate of about 90 percent. A motivation for the development of this system is to provide an alternate text input mechanism for camera enabled handheld devices, such as video mobile phones and PDAs.
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| Subjects |
280208 Computer Vision
280104 Computer-Human Interaction
280207 Pattern Recognition
E1
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| Keyword(s) |
iris-research
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| Additional Notes |
[CDROM]
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