Online K-Means Algorithm for Background Subtraction

DOI

10.2991/icmmcce-15.2015.137How to use a DOI?

Keywords

Gaussian mixture model; on-line K-means; background subtraction

Abstract

Background subtraction is an important step in video processing. GMM algorithm uses Gaussian mixture model to identify moving objects and efficient equations have been derived to update GMM parameters. In order to compute parameters more accurately while maintain constant computing time per frame, we apply online K-Means algorithm to update the parameters of Gaussian mixture models and the corresponding incremental K-means equations are derived. Experiments demonstrate that online K-means algorithm can give more efficient segment result than previous update equations.

Copyright

© 2015, 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  - Peng Chen
AU  - Beibei Jin
AU  - Xiangbing Zhu
AU  - Mingxing Fang
PY  - 2015/12
DA  - 2015/12
TI  - Online K-Means Algorithm for Background Subtraction
BT  - Proceedings of the 4th International Conference on Mechatronics, Materials, Chemistry and Computer Engineering 2015
PB  - Atlantis Press
SP  - 681
EP  - 685
SN  - 2352-538X
UR  - https://doi.org/10.2991/icmmcce-15.2015.137
DO  - 10.2991/icmmcce-15.2015.137
ID  - Chen2015/12
ER  -