Abstract
Vision-based gesture Recognition accords with natural communication habits of human and can carry out long-distance and non-contact interactions. So it has become a hot direction in human-computer interaction research whose Recognition effect largely depends on the performance of image preprocessing and Recognition algorithms. In this paper, a gesture Recognition method using color image and depth image combined is designed. For the influence of the angle on the same gesture, the skeleton algorithm is optimized based on the layer-by-layer stripping concept. The fast refinement algorithm improves the process of repeated scanning, extracts the key node information in the skeleton map of the hand, and establishes the spatial axis of the hand to determine the gesture direction. The gesture Recognition experiment was performed based on convolutional neural network. The results showed the Recognition accuracy rate was 96.01%, and the robustness and accuracy of the proposed Recognition method were verified.
Original language | English |
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Title of host publication | Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018 |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 4041-4046 |
Number of pages | 6 |
ISBN (Electronic) | 978-1-5386-6650-0 |
ISBN (Print) | 978-1-5386-6651-7 |
DOIs | |
Publication status | Published - 17 Jan 2019 |
Event | 2018 IEEE International Conference on Systems, Man and Cybernetics - Miyazaki, Japan Duration: 7 Oct 2018 → 10 Oct 2018 http://www.smc2018.org/ |
Publication series
Name | IEEE SMC Proceedings Series |
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Publisher | IEEE |
ISSN (Print) | 1062-922X |
ISSN (Electronic) | 2577-1655 |
Conference
Conference | 2018 IEEE International Conference on Systems, Man and Cybernetics |
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Abbreviated title | SMC 2018 |
Country/Territory | Japan |
City | Miyazaki |
Period | 7/10/18 → 10/10/18 |
Internet address |
Keywords
- Convolutional neural network
- Depth information
- Gesture Recognition
- Hand skeleton extraction