Proceedings of the 2017 6th International Conference on Measurement, Instrumentation and Automation (ICMIA 2017)

A New Application on Gaussian Mixture Modeling in Object Detection

Authors
Dawei Qiu, Jing Liu, Hui Cao
Corresponding Author
Dawei Qiu
Available Online June 2017.
DOI
https://doi.org/10.2991/icmia-17.2017.76How to use a DOI?
Keywords
Object Detection, Gaussian Mixture Model, Maximum Likelihood.
Abstract
For background modeling, the conventional Gaussian Mixture Model (GMM) is a popular approach. However, because of the inappropriate parameters updating method, GMM often suffers from a problem that it cannot classify a pixel into background or foreground correctly for longtime. In the paper, we proposed a new parameters updating method for GMM, and built background model for every pixel and global foreground model for the entire image. We presented an improved object detection and tracking scheme based on the proposed approach. The experimental results show the proposed GMM parameters updating method, together with the object detection and tracking framework, give better performance than the conventional Gaussian Mixture Modeling algorithm.
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Proceedings
2017 6th International Conference on Measurement, Instrumentation and Automation (ICMIA 2017)
Part of series
Advances in Intelligent Systems Research
Publication Date
June 2017
ISBN
978-94-6252-387-6
ISSN
1951-6851
DOI
https://doi.org/10.2991/icmia-17.2017.76How 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  - Dawei Qiu
AU  - Jing Liu
AU  - Hui Cao
PY  - 2017/06
DA  - 2017/06
TI  - A New Application on Gaussian Mixture Modeling in Object Detection
BT  - 2017 6th International Conference on Measurement, Instrumentation and Automation (ICMIA 2017)
PB  - Atlantis Press
SN  - 1951-6851
UR  - https://doi.org/10.2991/icmia-17.2017.76
DO  - https://doi.org/10.2991/icmia-17.2017.76
ID  - Qiu2017/06
ER  -