Abstract
A pattern recognition approach is proposed for the Chinese digital gesture. We shot a group of digital gesture videos by a monocular camera. Then, the video was converted into frame format and turned into the gray image. We selected the gray image as our own dataset. The dataset was divided into six gesture classes and other meaningless gestures. We use the neural network (NN) combining convolution and Max-Pooling (MPCNN) for classification of digital gestures. The MPCNN presents some differences on the data preprocessing, the activation function and the network structure. The accuracy and the robustness have been verified by the simulation experiments with the dataset. The result shows that the MPCNN classifies six gesture classes with 99.98 % accuracy using the Max-Pooling, the Relu activation function, and the binarization processing.
Zhu Mengyu—This work is supported by the national High Technology Research and Development Program of China (2015AA042300).
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Qian, Z. et al. (2017). Max-Pooling Convolutional Neural Network for Chinese Digital Gesture Recognition. In: Balas, V., Jain, L., Zhao, X. (eds) Information Technology and Intelligent Transportation Systems. Advances in Intelligent Systems and Computing, vol 455. Springer, Cham. https://doi.org/10.1007/978-3-319-38771-0_8
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