@inproceedings{44672318d9a44b44a1e34dabe76beffb,
title = "Max-pooling convolutional neural network for Chinese digital gesture recognition",
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.",
keywords = "Activation function, Chinese digital gesture recognition, Convolutional neural network, Data preprocessing",
author = "Zhao Qian and Li Yawei and Zhu Mengyu and Yang Yuliang and Xiao Ling and Xu Chunyu and Li Lin",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing Switzerland 2017.; International Conference on Information Technology and Intelligent Transportation Systems, ITITS 2015 ; Conference date: 12-12-2015 Through 13-12-2015",
year = "2017",
doi = "10.1007/978-3-319-38771-0_8",
language = "English",
isbn = "9783319387697",
series = "Advances in Intelligent Systems and Computing",
publisher = "Springer Verlag",
pages = "79--89",
editor = "Jain, {Lakhmi C.} and Xiangmo Zhao and Balas, {Valentina Emilia}",
booktitle = "Information Technology and Intelligent Transportation System - Volume 2, Proceedings of the International Conference on Information Technology and Intelligent Transportation Systems, ITITS 2015",
address = "Germany",
}