@inproceedings{b5dc7de9cdc148e6a7c9bdca086d1b67,
title = "Research on human action recognition based on convolutional neural network",
abstract = "This paper proposes a human action recognition (HAR) algorithm based on convolutional neural network, which is used for human semaphore motion recognition. First, collecting datas in three scenarios and Deep Convolution Generative Adversarial Networks(DCGAN) is used to implement data enhancement to generate the dataset (DataSR). Then, the 1∗1 and 3∗3 convolution kernels are used to design the full convolution network and the model is further compressed using the group convolution to obtain the new model HARNET. Experiments show that the mAP of HARNET on the DataSR dataset is 94.36%, and the model size is 76M, which is 30% of the size of the YOLOv3 model.",
keywords = "DCGAN, DataSR, Group convolution, Human action recognition",
author = "Peng Wang and Yuliang Yang and Wanchong Li and Linhao Zhang and Mengyuan Wang and Xiaobo Zhang and Mengyu Zhu",
note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 28th Wireless and Optical Communications Conference, WOCC 2019 ; Conference date: 09-05-2019 Through 10-05-2019",
year = "2019",
month = may,
doi = "10.1109/WOCC.2019.8770575",
language = "English",
series = "2019 28th Wireless and Optical Communications Conference, WOCC 2019 - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2019 28th Wireless and Optical Communications Conference, WOCC 2019 - Proceedings",
address = "United States",
}