Proceedings of the Advances in Materials, Machinery, Electrical Engineering (AMMEE 2017)

Online Learning Classification for Video Monitor

Authors
Zhiyuan Li, Chao Wang, Xiaoduo Zhang
Corresponding Author
Zhiyuan Li
Available Online June 2017.
DOI
https://doi.org/10.2991/ammee-17.2017.62How to use a DOI?
Keywords
Online learning, Unsupervised, Objects classification, Video Monitor.
Abstract
This paper presents an online unsupervised learning classification of pedestrians and vehicles for video Monitor. Different from traditional methods depending on offline training, our method adopts the online label strategy based on temporal and morphological features, which saves time and labor to a large extent. It extract the moving objects with their features from the original video. An online filtering procedure is adopted to label the moving objects according to certain threshold of speed and area feature. The labeled objects are sent into a SVM classifier to generate the pedestrian & vehicle classifier. Experimental results illustrate that our unsupervised learning algorithm is adapted to polymorphism of the pedestrians and diversity of the vehicles with high classification accuracy.
Open Access
This is an open access article distributed under the CC BY-NC license.

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Proceedings
Advances in Materials, Machinery, Electrical Engineering (AMMEE 2017)
Part of series
Advances in Engineering Research
Publication Date
June 2017
ISBN
978-94-6252-350-0
ISSN
2352-5401
DOI
https://doi.org/10.2991/ammee-17.2017.62How 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  - Zhiyuan Li
AU  - Chao Wang
AU  - Xiaoduo Zhang
PY  - 2017/06
DA  - 2017/06
TI  - Online Learning Classification for Video Monitor
BT  - Advances in Materials, Machinery, Electrical Engineering (AMMEE 2017)
PB  - Atlantis Press
SN  - 2352-5401
UR  - https://doi.org/10.2991/ammee-17.2017.62
DO  - https://doi.org/10.2991/ammee-17.2017.62
ID  - Li2017/06
ER  -