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An Adaptive Superpixel Based Hand Gesture Tracking and Recognition System
Joint Authors
Source
Issue
Vol. 2014, Issue 2014 (31 Dec. 2014), pp.1-12, 12 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2014-05-27
Country of Publication
Egypt
No. of Pages
12
Main Subjects
Medicine
Information Technology and Computer Science
Abstract EN
We propose an adaptive and robust superpixel based hand gesture tracking system, in which hand gestures drawn in free air are recognized from their motion trajectories.
First we employed the motion detection of superpixels and unsupervised image segmentation to detect the moving target hand using the first few frames of the input video sequence.
Then the hand appearance model is constructed from its surrounding superpixels.
By incorporating the failure recovery and template matching in the tracking process, the target hand is tracked by an adaptive superpixel based tracking algorithm, where the problem of hand deformation, view-dependent appearance invariance, fast motion, and background confusion can be well handled to extract the correct hand motion trajectory.
Finally, the hand gesture is recognized by the extracted motion trajectory with a trained SVM classifier.
Experimental results show that our proposed system can achieve better performance compared to the existing state-of-the-art methods with the recognition accuracy 99.17% for easy set and 98.57 for hard set.
American Psychological Association (APA)
Zhu, Hong-Min& Pun, Chi-Man. 2014. An Adaptive Superpixel Based Hand Gesture Tracking and Recognition System. The Scientific World Journal،Vol. 2014, no. 2014, pp.1-12.
https://search.emarefa.net/detail/BIM-1051331
Modern Language Association (MLA)
Zhu, Hong-Min& Pun, Chi-Man. An Adaptive Superpixel Based Hand Gesture Tracking and Recognition System. The Scientific World Journal No. 2014 (2014), pp.1-12.
https://search.emarefa.net/detail/BIM-1051331
American Medical Association (AMA)
Zhu, Hong-Min& Pun, Chi-Man. An Adaptive Superpixel Based Hand Gesture Tracking and Recognition System. The Scientific World Journal. 2014. Vol. 2014, no. 2014, pp.1-12.
https://search.emarefa.net/detail/BIM-1051331
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1051331