一种基于改进SIFT的视频稳像方法
A Video Stabilization Method Based on Improved SIFT
李欣 1焦立男 1柳有权 1马彩莎1
作者信息
- 1. 长安大学信息工程学院,陕西 西安 710018
- 折叠
摘要
为提高计算效率并保持良好的稳像效果,本文提出一种基于改进SIFT的视频稳像方法.首先对SIFT进行改进,并命名为BO-SIFT(Binarized Octagonal SIFT).该算法引入了同心八边形环特征描述子,通过降维和二值化对特征向量进行处理,然后使用汉明距离进行特征点匹配,有效缩短了描述和匹配时间.其次将BO-SIFT算法应用于视频稳像,提取视频帧的特征点进行匹配,并计算出帧与帧之间的运动偏移量,以此实现运动估计.最后采用卡尔曼滤波器对估计出的运动偏移量进行平滑处理,并利用仿射变换对视频帧进行逆向补偿,从而得到稳定的图像序列.实验结果表明:相较于原始的SIFT算法,BO-SIFT算法使稳像时间减少了56.404%;相较于现有的较好算法,BO-SIFT算法稳像后的视频具有更高的平均峰值信噪比.此外,本文算法在不同视频上进行稳像效果测试,也具有一定的可靠性和优越性.
Abstract
This paper proposes a video stabilization method based on improved SIFT to improve computational efficiency and maintain a good video stabilization effect.Firstly,SIFT is improved and named BO-SIFT(Binarized Octagonal SIFT).The algo-rithm introduces concentric octagonal ring feature descriptors,processes the feature vectors by dimensionality reduction and bina-rization,and then uses Hamming distance for feature point matching,which effectively reduces the description and matching time.Secondly,the BO-SIFT algorithm is applied to video stabilization,extracting the feature points of the video frames for matching and calculating the motion offsets between frames to achieve motion estimation.Afterwards,the estimated motion off-sets are smoothed using a Kalman filter and the video frames are inversely compensated using affine transformation to obtain a sta-bilized image sequence.The experimental results show that the BO-SIFT algorithm reduces the stabilization time by 56.404%compared to the original SIFT algorithm,and the stabilized video of the BO-SIFT algorithm has a higher average peak signal-to-noise ratio compared to the existing better algorithms.In addition,the algorithm in this paper is tested on different videos for video stabilization effects,which also has certain reliability and superiority.
关键词
视频稳像/BO-SIFT算法/降维/二值化/运动估计/峰值信噪比Key words
video stabilization/BO-SIFT algorithm/dimensionality reduction/binarization/motion estimation/peak signal-to-noise ratio引用本文复制引用
基金项目
国家科技重点研发计划项目(2018YFB1600802)
出版年
2024
国家科技期刊平台
NSTL
万方数据