Proceedings of the International Conference on Communication and Electronic Information Engineering (CEIE 2016)

Falling-Action Analysis Algorithm Based on Convolutional Neural Network

Authors
Wei Liu, Jie Guo, Zheng Huang, Weidong Qiu
Corresponding Author
Wei Liu
Available Online October 2016.
DOI
https://doi.org/10.2991/ceie-16.2017.5How to use a DOI?
Keywords
Falling-Action Analysis; Deep Learning; Convolutional Neural Network
Abstract
This paper proposes a deep learning method - convolutional neural network to analyze human falling-action in video surveillance, so that we can recognize the falling-action of human body accurately in the shortest time. Firstly, vibe algorithm is used to extract the foreground and some methods of image preprocessing are employed to optimize the moving target. Then the moving target is fed into the convolutional neural network which extracts the features of various actions (including sitting, crouching, bending, falling) and classifies these actions. It is proved by experiments that our method is accurate and competitive compared with the current method to falling-action recognition.
Open Access
This is an open access article distributed under the CC BY-NC license.

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Volume Title
Proceedings of the International Conference on Communication and Electronic Information Engineering (CEIE 2016)
Series
Advances in Engineering Research
Publication Date
October 2016
ISBN
978-94-6252-312-8
ISSN
2352-5401
DOI
https://doi.org/10.2991/ceie-16.2017.5How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

TY  - CONF
AU  - Wei Liu
AU  - Jie Guo
AU  - Zheng Huang
AU  - Weidong Qiu
PY  - 2016/10
DA  - 2016/10
TI  - Falling-Action Analysis Algorithm Based on Convolutional Neural Network
BT  - Proceedings of the International Conference on Communication and Electronic Information Engineering (CEIE 2016)
PB  - Atlantis Press
SP  - 37
EP  - 42
SN  - 2352-5401
UR  - https://doi.org/10.2991/ceie-16.2017.5
DO  - https://doi.org/10.2991/ceie-16.2017.5
ID  - Liu2016/10
ER  -