Proceedings of the 2019 International Conference on Computer, Network, Communication and Information Systems (CNCI 2019)

Research on Behavior Recognition in Infrared Video

Authors
Ruming Yang, Meng Ding, Xu Zhang, Xinyan Jiang
Corresponding Author
Meng Ding
Available Online May 2019.
DOI
10.2991/cnci-19.2019.34How to use a DOI?
Keywords
Convolutional neural network, infrared video, transfer learning, visualization.
Abstract

We trained a neural network for behavior recognition task in infrared video and took a small amount of infrared video to evaluate performance. Limited by the quantity of data, we pre-trained the network on the visible datasets and fine-tuned on our infrared dataset. In order to explore the effect of the learning of the two datasets, we visualized the features. The experimental results demonstrated that our network has excellent learning performance for infrared video, and the learned features are efficient and generalized.

Copyright
© 2019, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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Volume Title
Proceedings of the 2019 International Conference on Computer, Network, Communication and Information Systems (CNCI 2019)
Series
Advances in Computer Science Research
Publication Date
May 2019
ISBN
10.2991/cnci-19.2019.34
ISSN
2352-538X
DOI
10.2991/cnci-19.2019.34How to use a DOI?
Copyright
© 2019, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - CONF
AU  - Ruming Yang
AU  - Meng Ding
AU  - Xu Zhang
AU  - Xinyan Jiang
PY  - 2019/05
DA  - 2019/05
TI  - Research on Behavior Recognition in Infrared Video
BT  - Proceedings of the 2019 International Conference on Computer, Network, Communication and Information Systems (CNCI 2019)
PB  - Atlantis Press
SP  - 232
EP  - 236
SN  - 2352-538X
UR  - https://doi.org/10.2991/cnci-19.2019.34
DO  - 10.2991/cnci-19.2019.34
ID  - Yang2019/05
ER  -