This paper presents a gesture recognition approach for CAD interfaces where the Leap Motion Controller is used for its high precision in modelling user hands. A simple, compact and effective hand representation is proposed to encode trajectory and pose across time. Recognition is based on Recurrent Neural Networks, particularly suited for processing data sequences. An effective data augmentation technique is also described to increase the size of the training set. Experiments conducted on a novel dataset of gesture performed by 30 volunteers show the effectiveness of the proposed technique; the dataset will be made available to the community for future studies.

Gesture recognition by leap motion controller and LSTM networks for CAD-oriented interfaces

MAZZINI, LISA
;
Franco A.;Maltoni D.
2019

Abstract

This paper presents a gesture recognition approach for CAD interfaces where the Leap Motion Controller is used for its high precision in modelling user hands. A simple, compact and effective hand representation is proposed to encode trajectory and pose across time. Recognition is based on Recurrent Neural Networks, particularly suited for processing data sequences. An effective data augmentation technique is also described to increase the size of the training set. Experiments conducted on a novel dataset of gesture performed by 30 volunteers show the effectiveness of the proposed technique; the dataset will be made available to the community for future studies.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
185
195
Mazzini L.; Franco A.; Maltoni D.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11585/707965
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