Proceedings of the 2016 6th International Conference on Mechatronics, Computer and Education Informationization (MCEI 2016)

A Method Based on Dense Trajectory for Violent Video Classification

Authors
Nan Wang, Wei Song, Jianjun Hou, Jing Yu
Corresponding Author
Nan Wang
Available Online December 2016.
DOI
https://doi.org/10.2991/mcei-16.2016.163How to use a DOI?
Keywords
Gradient; Optical flow; Dense trajectory; Extreme learning machine; Bag of words
Abstract
At present, the internet technology develops so rapidly and the video becomes the major component of the internet traffic. The content security of massive public videos is an important factor to the social stability. Among them, violent video is an important class of unsafe videos. We proposed a novel method based on dense trajectory and extreme learning machine to recognize them. The spatial-temporal characteristics were well expressed by the use of optical flow and gradient. The experiment on the benchmark dataset named Movies indicated our proposed method had a better accuracy than the state-of-the-art methods. Our proposed method is an efficient method for violent video classification.
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This is an open access article distributed under the CC BY-NC license.

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Proceedings
2016 6th International Conference on Mechatronics, Computer and Education Informationization (MCEI 2016)
Part of series
Advances in Intelligent Systems Research
Publication Date
December 2016
ISBN
978-94-6252-282-4
ISSN
1951-6851
DOI
https://doi.org/10.2991/mcei-16.2016.163How 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  - Nan Wang
AU  - Wei Song
AU  - Jianjun Hou
AU  - Jing Yu
PY  - 2016/12
DA  - 2016/12
TI  - A Method Based on Dense Trajectory for Violent Video Classification
BT  - 2016 6th International Conference on Mechatronics, Computer and Education Informationization (MCEI 2016)
PB  - Atlantis Press
SP  - 781
EP  - 786
SN  - 1951-6851
UR  - https://doi.org/10.2991/mcei-16.2016.163
DO  - https://doi.org/10.2991/mcei-16.2016.163
ID  - Wang2016/12
ER  -