2019年
AN IMPROVED HAND GESTURE RECOGNITION WITH TWO-STAGE CONVOLUTION NEURAL NETWORKS USING A HAND COLOR IMAGE AND ITS PSEUDO-DEPTH IMAGE
2019 IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP)
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- 開始ページ
- 375
- 終了ページ
- 379
- 記述言語
- 英語
- 掲載種別
- 研究論文(国際会議プロシーディングス)
- DOI
- 10.1109/ICIP.2019.8802970
- 出版者・発行元
- IEEE
Robust hand gesture recognition has been playing a significant role in the field of human-computer interaction for a long time, but it is still full of challenges due to many accept such as cluttered backgrounds and hand self-occlusion. With the help of depth information, depth-based methods have better performance, but the depth cameras are not as widely used and affordable as color cameras. Therefore, in this paper, we propose a two-stage deep convolutional neural network (CNN) architecture for accurate color-based hand gesture recognition. The first stage performs generation of pseudo-depth hand images from color images and the second stage recognizes hand gesture classes using both the color image and its pseudo-depth hand image. The generation stage architecture is based on an image-to-image translation network. In the recognition stage, a two-stream CNN architecture with color image and its pseudo depth image is proposed to improve the color image-based recognition performance. We also propose two strategies in two-stream fusion: feature fusion and committee fusion. To validate our approach, we construct a new dataset called MaHG-RGBD dataset. Experiments demonstrate that our approach significantly improves the performance in RGB-only recognition for hand gestures.
- リンク情報
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- DOI
- https://doi.org/10.1109/ICIP.2019.8802970
- DBLP
- https://dblp.uni-trier.de/rec/conf/icip/LiuFTIC19
- Web of Science
- https://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=JSTA_CEL&SrcApp=J_Gate_JST&DestLinkType=FullRecord&KeyUT=WOS:000521828600075&DestApp=WOS_CPL
- Dblp Cross Ref
- https://dblp.uni-trier.de/conf/icip/2019
- Dblp Url
- https://dblp.uni-trier.de/db/conf/icip/icip2019.html#LiuFTIC19
- ID情報
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- DOI : 10.1109/ICIP.2019.8802970
- ISSN : 1522-4880
- DBLP ID : conf/icip/LiuFTIC19
- Web of Science ID : WOS:000521828600075