Hand Motion and Posture Recognition in a Network of Calibrated Cameras

Resource type
Date created
2017-10-31
Authors/Contributors
Abstract
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.
Document
Published as
Jingya Wang and Shahram Payandeh, “Hand Motion and Posture Recognition in a Network of Calibrated Cameras,” Advances in Multimedia, vol. 2017, Article ID 2162078, 25 pages, 2017. DOI: 10.1155/2017/2162078.
Publication title
Advances in Multimedia
Document title
“Hand Motion and Posture Recognition in a Network of Calibrated Cameras,”
Date
2017
Volume
2017
First page
1
Last page
25
Publisher DOI
10.1155/2017/2162078
Copyright statement
Copyright is held by the author(s).
Scholarly level
Peer reviewed?
Yes
Language
Member of collection
Attachment Size
2162078.pdf 8.65 MB