NDLI: Hand gesture recognition with 3D convolutional neural networks

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2015 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

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Hand gesture recognition with 3D convolutional neural networks

Content Provider	IEEE Xplore Digital Library
Author	Molchanov, P. Gupta, S. Kihwan Kim Kautz, J.
Copyright Year	2015
Description	Author affiliation: NVIDIA, Santa Clara, CA, USA (Molchanov, P.; Gupta, S.; Kihwan Kim; Kautz, J.)
Abstract	Touchless hand gesture recognition systems are becoming important in automotive user interfaces as they improve safety and comfort. Various computer vision algorithms have employed color and depth cameras for hand gesture recognition, but robust classification of gestures from different subjects performed under widely varying lighting conditions is still challenging. We propose an algorithm for drivers' hand gesture recognition from challenging depth and intensity data using 3D convolutional neural networks. Our solution combines information from multiple spatial scales for the final prediction. It also employs spatio-temporal data augmentation for more effective training and to reduce potential overfitting. Our method achieves a correct classification rate of 77.5% on the VIVA challenge dataset.
Starting Page	1
Ending Page	7
File Size	1083795
Page Count	7
File Format	PDF
ISSN	21607516
e-ISBN	9781467367592
DOI	10.1109/CVPRW.2015.7301342
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-06-07
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Kernel
Content Type	Text
Resource Type	Article

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