IEICE TRANSACTIONS on Fundamentals
A Framework of Real Time Hand Gesture Vision Based Human-Computer Interaction
Summary :
This paper presents a robust framework of human-computer interaction from the hand gesture vision in the presence of realistic and challenging scenarios. To this end, several novel components are proposed. A hybrid approach is first proposed to automatically infer the beginning position of hand gestures of interest via jointly optimizing the regions given by an offline skin model trained from Gaussian mixture models and a specific hand gesture classifier trained from the Adaboost technique. To consistently track the hand in the context of using kernel based tracking, a semi-supervised feature selection strategy is further presented to choose the feature subspaces which appropriately represent the properties of offline hand skin cues and online foreground-background-classification cues. Taking the histogram of oriented gradients as the descriptor to represent hand gestures, a soft-decision approach is finally proposed for recognizing static hand gestures at the locations where severe ambiguity occurs and hidden Markov model based dynamic gestures are employed for interaction. Experiments on various real video sequences show the superior performance of the proposed components. In addition, the whole framework is applicable to real-time applications on general computing platforms.
- Publication
- IEICE TRANSACTIONS on Fundamentals Vol.E94-A No.3 pp.979-989
- Publication Date
- 2011/03/01
- Publicized
- Online ISSN
- 1745-1337
- DOI
- 10.1587/transfun.E94.A.979
- Type of Manuscript
- PAPER
- Category
- Vision
Authors
Keyword
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Liang SHA, Guijin WANG, Xinggang LIN, Kongqiao WANG, "A Framework of Real Time Hand Gesture Vision Based Human-Computer Interaction" in IEICE TRANSACTIONS on Fundamentals,
vol. E94-A, no. 3, pp. 979-989, March 2011, doi: 10.1587/transfun.E94.A.979.
Abstract: This paper presents a robust framework of human-computer interaction from the hand gesture vision in the presence of realistic and challenging scenarios. To this end, several novel components are proposed. A hybrid approach is first proposed to automatically infer the beginning position of hand gestures of interest via jointly optimizing the regions given by an offline skin model trained from Gaussian mixture models and a specific hand gesture classifier trained from the Adaboost technique. To consistently track the hand in the context of using kernel based tracking, a semi-supervised feature selection strategy is further presented to choose the feature subspaces which appropriately represent the properties of offline hand skin cues and online foreground-background-classification cues. Taking the histogram of oriented gradients as the descriptor to represent hand gestures, a soft-decision approach is finally proposed for recognizing static hand gestures at the locations where severe ambiguity occurs and hidden Markov model based dynamic gestures are employed for interaction. Experiments on various real video sequences show the superior performance of the proposed components. In addition, the whole framework is applicable to real-time applications on general computing platforms.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/transfun.E94.A.979/_p
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@ARTICLE{e94-a_3_979,
author={Liang SHA, Guijin WANG, Xinggang LIN, Kongqiao WANG, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={A Framework of Real Time Hand Gesture Vision Based Human-Computer Interaction},
year={2011},
volume={E94-A},
number={3},
pages={979-989},
abstract={This paper presents a robust framework of human-computer interaction from the hand gesture vision in the presence of realistic and challenging scenarios. To this end, several novel components are proposed. A hybrid approach is first proposed to automatically infer the beginning position of hand gestures of interest via jointly optimizing the regions given by an offline skin model trained from Gaussian mixture models and a specific hand gesture classifier trained from the Adaboost technique. To consistently track the hand in the context of using kernel based tracking, a semi-supervised feature selection strategy is further presented to choose the feature subspaces which appropriately represent the properties of offline hand skin cues and online foreground-background-classification cues. Taking the histogram of oriented gradients as the descriptor to represent hand gestures, a soft-decision approach is finally proposed for recognizing static hand gestures at the locations where severe ambiguity occurs and hidden Markov model based dynamic gestures are employed for interaction. Experiments on various real video sequences show the superior performance of the proposed components. In addition, the whole framework is applicable to real-time applications on general computing platforms.},
keywords={},
doi={10.1587/transfun.E94.A.979},
ISSN={1745-1337},
month={March},}
Copy
TY - JOUR
TI - A Framework of Real Time Hand Gesture Vision Based Human-Computer Interaction
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 979
EP - 989
AU - Liang SHA
AU - Guijin WANG
AU - Xinggang LIN
AU - Kongqiao WANG
PY - 2011
DO - 10.1587/transfun.E94.A.979
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E94-A
IS - 3
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - March 2011
AB - This paper presents a robust framework of human-computer interaction from the hand gesture vision in the presence of realistic and challenging scenarios. To this end, several novel components are proposed. A hybrid approach is first proposed to automatically infer the beginning position of hand gestures of interest via jointly optimizing the regions given by an offline skin model trained from Gaussian mixture models and a specific hand gesture classifier trained from the Adaboost technique. To consistently track the hand in the context of using kernel based tracking, a semi-supervised feature selection strategy is further presented to choose the feature subspaces which appropriately represent the properties of offline hand skin cues and online foreground-background-classification cues. Taking the histogram of oriented gradients as the descriptor to represent hand gestures, a soft-decision approach is finally proposed for recognizing static hand gestures at the locations where severe ambiguity occurs and hidden Markov model based dynamic gestures are employed for interaction. Experiments on various real video sequences show the superior performance of the proposed components. In addition, the whole framework is applicable to real-time applications on general computing platforms.
ER -
English
