Abstract
As a new type of human-robot interaction (HRI), hand gesture has many advantages such as natural operation, rich expression and not subject to environ-mental constraints. So it is very suitable for space human-robot interaction tasks in special and harsh environment. Considering that static hand gesture is one of the main gesture expressions in human-computer interaction, so a parallel convolution neural networks (CNNs) is designed to improve the accuracy of static hand gesture recognition in the conditions of complex background and changing illumination. In addition, the method is applied to the operation of space human-robot system with hand gesture control. Various space HRI hand gestures from different subjects are evaluated and tested, and experimental results demonstrate that the proposed method outperforms the single-channel CNN methods and other popular methods with a higher accuracy.
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Acknowledgments
This work is supported by Research Fund of China Manned Space Engineering (050102), the Key Research Program of the Chinese Academy of Sciences (Y4A3210301), the National Science Foundation of China (51175494, 61128008, and 51575412), and the State Key Laboratory of Robotics Foundation.
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Gao, Q., Liu, J., Ju, Z., Li, Y., Zhang, T., Zhang, L. (2017). Static Hand Gesture Recognition with Parallel CNNs for Space Human-Robot Interaction. In: Huang, Y., Wu, H., Liu, H., Yin, Z. (eds) Intelligent Robotics and Applications. ICIRA 2017. Lecture Notes in Computer Science(), vol 10462. Springer, Cham. https://doi.org/10.1007/978-3-319-65289-4_44
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DOI: https://doi.org/10.1007/978-3-319-65289-4_44
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