Online K-Means Algorithm for Background Subtraction
Peng Chen, Beibei Jin, Xiangbing Zhu, Mingxing Fang
Available Online December 2015.
- https://doi.org/10.2991/icmmcce-15.2015.137How to use a DOI?
- Gaussian mixture model; on-line K-means; background subtraction
- 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.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
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 - https://doi.org/10.2991/icmmcce-15.2015.137 ID - Chen2015/12 ER -