基于改进加速鲁棒特征的井下视频拼接算法

Underground video stitching algorithm based on improved speeded up robust features

针对加速鲁棒特征算法用于井下视频拼接时实时性不高的问题,通过降低特征点维度和仅在感兴趣区域提取特征点来改进加速鲁棒特征算法,在此基础上提出了一种井下视频拼接算法。首先,利用改进的加速鲁棒特征算法提取视频图像特征点;然后,动态追踪特征点数量,若非首帧图像特征点数量变化超过阈值,则重新进行特征点配准、提纯,以及投影变换矩阵计算及存储处理,否则采用前一帧图像所得的投影变换矩阵;最后,采用渐入渐出加权平均法进行图像融合处理,完成视频拼接。实验结果表明,基于改进加速鲁棒特征的井下视频拼接算法实时性高,拼接效果较好。

For the problem of low real-time performance of speeded up robust features(SURF)algorithm used in underground video stitching,the SURF algorithm was improved by decreasing dimensions of feature points and extracting feature points only in region of interest.On this basis,an underground video stitching algorithm was proposed.Firstly,feature points of video images are extracted by using the improved SURF algorithm.Then the number of feature points is dynamically tracked.If the number of feature points in the non-first frame image exceeds the threshold,some operations will be performed again including feature point matching and purifying and calculation and storage of projective transformation matrix.Otherwise,the projective transformation matrix from the previous frame is used.Finally,images are fused by gradual weighted average fusion method to generate a stitched video.The experimental results show that the underground video stitching algorithm based on the improved SURF algorithm has high real-time performance and good stitching effect.