@inproceedings{928ac6139f254218ad91bda986427364,
title = "Dynamic gesture recognition based on CNN-LSTM-Attention",
abstract = "Compared with traditional human-computer interaction techniques, gesture recognition is closer to human expression habits and have some advantages of being efficient and easy to master. Vision-based gesture recognition does not require additional equipment, and is very convenient and relatively low cost. To recognize dynamic gesture in complex background, we build a backbone network based on SSD with dilated convolution, which greatly improves the quality of the detected feature maps, and then we proposes a CNN-LSTM-Attention based dynamic gesture recognition network. The spatial features of dynamic gestures at each moment are first extracted from gesture sequences, then these features are transformed into dynamic gesture spatio-Temporal features by a recurrent neural network with an attention mechanism, and finally fed into a fully connected neural network for gesture recognition. The dynamic gesture recognition network achieves 93.5% recognition rate on Sahand dataset, which exhibits its effectiveness.",
keywords = "attention mechanism, deep Learning, dilated convolution, gesture recognition, LSTM",
author = "Jinwei Liu and Baoguo Wei and Mingzhi Cai and Yong Xu",
note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 2021 IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2021 ; Conference date: 17-08-2021 Through 19-08-2021",
year = "2021",
month = aug,
day = "17",
doi = "10.1109/ICSPCC52875.2021.9565034",
language = "英语",
series = "Proceedings of 2021 IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2021",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "Proceedings of 2021 IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2021",
}