Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
4947911 | Neurocomputing | 2017 | 15 Pages |
Abstract
In many dynamic hand gesture recognition contexts, time information is not adequately used. The extracted features of dynamic gestures usually do not carry explicit information about time in gesture classification. This results in under-utilized data for more important accurate classification. Another disadvantage is that the gesture classification is then confined to only simple gestures. We have overcome these limitations by introducing centroid tracking of hand gestures that captures and retains the time sequence information for feature extraction. This simplifies the classification of dynamic gestures as movement in time helps efficient classification without burdensome processing.
Related Topics
Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
Prashan Premaratne, Shuai Yang, Peter Vial, Zubair Ifthikar,