@inproceedings{05c976a1def244c9b51fe15801fef223,
title = "Spatial-temporal neural networks for action recognition",
abstract = "Action recognition is an important yet challenging problem in many applications. Recently, neural network and deep learning approaches have been widely applied to action recognition and yielded impressive results. In this paper, we present a spatial-temporal neural network model to recognize human actions in videos. This network is composed of two connected structures. A two-stream-based network extracts appearance and optical flow features from video frames. This network characterizes spatial information of human actions in videos. A group of LSTM structures following the spatial network describe the temporal information of human actions. We test our model with data from two public datasets and the experimental results show that our method improves the action recognition accuracy compared to the baseline methods.",
keywords = "Action recognition, LSTM, Spatial-temporal structure",
author = "Chao Jing and Ping Wei and Hongbin Sun and Nanning Zheng",
note = "Publisher Copyright: {\textcopyright} IFIP International Federation for Information Processing 2018 Published by Springer International Publishing AG 2018. All Rights Reserved.; 14th IFIP WG 12.5 International Conference on Artificial Intelligence Applications and Innovations, AIAI 2018 ; Conference date: 25-05-2018 Through 27-05-2018",
year = "2018",
doi = "10.1007/978-3-319-92007-8\_52",
language = "英语",
isbn = "9783319920061",
series = "IFIP Advances in Information and Communication Technology",
publisher = "Springer New York LLC",
pages = "619--627",
editor = "Lazaros Iliadis and Vassilis Plagianakos and Ilias Maglogiannis",
booktitle = "Artificial Intelligence Applications and Innovations - 14th IFIP WG 12.5 International Conference, AIAI 2018, Proceedings",
}