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Robust global motion estimation for video security based on improved k-means clustering

Authors :
Wan Xiangkui
Li Xiang
Minghu Wu
Juan Wang
Min Liu
Li Zhu
Rao Zheheng
Cong Liu
Nan Zhao
Source :
Journal of Ambient Intelligence and Humanized Computing. 10:439-448
Publication Year :
2018
Publisher :
Springer Science and Business Media LLC, 2018.

Abstract

The global motion vectors estimation is the most critical step for eliminating undesirable disturbances in unsafe video. In this paper, we proposed a novel global motion estimation approach based on improved K-means clustering algorithm to acquire trustworthy sequences. Firstly, the speeded up robust feature algorithm is employed to match feature points between two adjacent frames, and then we calculate the motion vectors of these matching points. Secondly, to remove the local motion vectors and reduce redundancy from the motion vectors, an improved K-means clustering algorithm is proposed. Thirdly, by using matching points from richest cluster, global motion vectors are calculated by homography transformation. The experimental simulation results demonstrate that the proposed method can obtain significantly higher computational efficiency and superior video security performance than traditional approaches.

Details

ISSN :
18685145 and 18685137
Volume :
10
Database :
OpenAIRE
Journal :
Journal of Ambient Intelligence and Humanized Computing
Accession number :
edsair.doi...........f6bebcdfba8871281b936cdf4470e1c1