Diver’s hand gesture recognition and segmentation for human–robot interaction on AUV

For the interaction between marine robots and divers in the underwater environment, a method of diver’s gesture recognition and segmentation is proposed. This method first uses the progressive growing training method to optimize the generative adversarial networks, generating high-resolution images...

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Bibliographic Details
Published in:Signal, image and video processing image and video processing, 2021-11, Vol.15 (8), p.1899-1906
Main Authors: Jiang, Yu, Zhao, Minghao, Wang, Chong, Wei, Fenglin, Wang, Kai, Qi, Hong
Format: Article
Language:English
Subjects:
Algorithms
Computer Imaging
Computer Science
Generative adversarial networks
Gesture recognition
High resolution
Image Processing and Computer Vision
Image resolution
Image segmentation
Multimedia Information Systems
Object recognition
Original Paper
Pattern Recognition and Graphics
Robots
Signal,Image and Speech Processing
Vision
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Summary:For the interaction between marine robots and divers in the underwater environment, a method of diver’s gesture recognition and segmentation is proposed. This method first uses the progressive growing training method to optimize the generative adversarial networks, generating high-resolution images with complex content. Then, we use the generative adversarial network model as a data augmentation method and generate high-resolution images. We make the masks of gestures in the new dataset and use the mask R-CNN algorithm for gesture recognition and gesture segmentation. The experimental results show that the generating data improves the accuracy of several object recognition algorithms but cannot completely replace the original data and the mean average precision of gesture recognition is 0.85. The visualization shows the validity and weakness of segmentation.
ISSN:1863-1703
1863-1711
DOI:10.1007/s11760-021-01930-5