A New Application on Gaussian Mixture Modeling in Object Detection

DOI

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.

Copyright

© 2017, 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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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  - Proceedings of the 2017 6th International Conference on Measurement, Instrumentation and Automation (ICMIA 2017)
PB  - Atlantis Press
SP  - 421
EP  - 424
SN  - 1951-6851
UR  - https://doi.org/10.2991/icmia-17.2017.76
DO  - 10.2991/icmia-17.2017.76
ID  - Qiu2017/06
ER  -