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Centroid tracking based dynamic hand gesture recognition using discrete Hidden Markov Models

Journal Article


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.

Publication Date


  • 2017

Citation


  • P. Premaratne, S. Yang, P. Vial & Z. Ifthikar, "Centroid tracking based dynamic hand gesture recognition using discrete Hidden Markov Models," Neurocomputing, vol. 228, pp. 79-83, 2017.

Scopus Eid


  • 2-s2.0-85006821030

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers/6339

Number Of Pages


  • 4

Start Page


  • 79

End Page


  • 83

Volume


  • 228

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.

Publication Date


  • 2017

Citation


  • P. Premaratne, S. Yang, P. Vial & Z. Ifthikar, "Centroid tracking based dynamic hand gesture recognition using discrete Hidden Markov Models," Neurocomputing, vol. 228, pp. 79-83, 2017.

Scopus Eid


  • 2-s2.0-85006821030

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers/6339

Number Of Pages


  • 4

Start Page


  • 79

End Page


  • 83

Volume


  • 228