@inproceedings{8eb33e22b1f24dba96efe989f530ed15,
title = "An Efficient Action Recognition Framework Based on ELM and 3D CNN",
abstract = "Deep neural network is shown to be the most efficient method for video representation and has achieved state-of-art results on different datasets of action recognition. In this paper, we proposed a hybrid architecture which integrates deep convolutional neural networks and extreme learning machine. The hybrid structure makes the most of their advantages: in the first stage the deep residual 3D network learns the features from both temporal and spatial sequences, then the ELM, instead of traditional classifiers, classifies the actions without tuning the parameters. The resulting network can not only extract the representation fully, but also obtain more accurate results faster. We show the effectiveness and outperformance of the proposed strategy on experiments.",
keywords = "Action recognition, Extreme learning machine, Hybrid structure",
author = "Yiping Zou and Xuemei Ren",
note = "Publisher Copyright: {\textcopyright} 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.; Chinese Intelligent Systems Conference, CISC 2020 ; Conference date: 24-10-2020 Through 25-10-2020",
year = "2021",
doi = "10.1007/978-981-15-8458-9_68",
language = "English",
isbn = "9789811584572",
series = "Lecture Notes in Electrical Engineering",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "641--648",
editor = "Yingmin Jia and Weicun Zhang and Yongling Fu",
booktitle = "Proceedings of 2020 Chinese Intelligent Systems Conference - Volume II",
address = "Germany",
}