Hand Motion and Posture Recognition in a Network of Calibrated Cameras
Joint Authors
Source
Issue
Vol. 2017, Issue 2017 (31 Dec. 2017), pp.1-25, 25 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2017-10-31
Country of Publication
Egypt
No. of Pages
25
Main Subjects
Information Technology and Computer Science
Abstract EN
This paper presents a vision-based approach for hand gesture recognition which combines both trajectory and hand posture recognition.
The hand area is segmented by fixed-range CbCr from cluttered and moving backgrounds and tracked by Kalman Filter.
With the tracking results of two calibrated cameras, the 3D hand motion trajectory can be reconstructed.
It is then modeled by dynamic movement primitives and a support vector machine is trained for trajectory recognition.
Scale-invariant feature transform is employed to extract features on segmented hand postures, and a novel strategy for hand posture recognition is proposed.
A gesture vector is introduced to recognize hand gesture as an entirety which combines the recognition results of motion trajectory and hand postures where a support vector machine is trained for gesture recognition based on gesture vectors.
American Psychological Association (APA)
Wang, Jingya& Payandeh, Shahram. 2017. Hand Motion and Posture Recognition in a Network of Calibrated Cameras. Advances in Multimedia،Vol. 2017, no. 2017, pp.1-25.
https://search.emarefa.net/detail/BIM-1122329
Modern Language Association (MLA)
Wang, Jingya& Payandeh, Shahram. Hand Motion and Posture Recognition in a Network of Calibrated Cameras. Advances in Multimedia No. 2017 (2017), pp.1-25.
https://search.emarefa.net/detail/BIM-1122329
American Medical Association (AMA)
Wang, Jingya& Payandeh, Shahram. Hand Motion and Posture Recognition in a Network of Calibrated Cameras. Advances in Multimedia. 2017. Vol. 2017, no. 2017, pp.1-25.
https://search.emarefa.net/detail/BIM-1122329
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1122329