电光与控制, 2017, 24 (5): 30, 网络出版: 2017-06-09   

基于景象匹配的无人机侦察视频快速配准方法

A Fast Registration Method for UAV Videos Based on Scene Matching
作者单位
军械工程学院, 石家庄 050003
摘要
为了改善无人机侦察视频配准效果与速度, 提出一种基于景象匹配的无人机侦察视频快速配准方法。首先提出基于AGAST-Difference与FREAK的特征匹配算法对视频帧之间配准, 然后提出匹配区域搜索方法在数字卫星地图上找到视频帧匹配区域, 最后将视频帧与匹配区域配准, 根据匹配区域二维关系计算单应矩阵完成拼接。实验结果表明,基于AGAST-Difference与FREAK的特征匹配算法在尺度、旋转、视点等变化及运行速度上存在很大优势, 匹配区域搜索方法避免了定位定向系统带来的误差与引入控制点, 提高了纠正精度与速度。本配准方法对像素大小为810×612的视频拼接速度达到25 帧/s, 在离地约1000 m的空中, 二维定位精度可达7.87 m。
Abstract
In order to improve the effect and speed of Unmanned Aerial Vehicle (UAV) reconnaissance video registration, a fast registration method based on scene matching is proposed.Firstly, a feature matching algorithm based on AGAST-Difference and Fast Retina Keypoint (FREAK) is proposed to match the video frames.Then, the matching region search method is proposed to find the matching region for the target frame in the satellite digital map.Finally, the video frame is registered to matching regions, and the homography matrix is calculated out according to 2D relationship of the matching regions for implementing the splicing.The experimental results show that:1) The feature matching algorithm based on AGAST-Difference and FREAK has great advantages in scale, rotation, viewpoint and so on;and 2) The matching region search method can avoid the error caused by the Position and Orientation System (POS) and the introduction of control points, thus the correction accuracy and speed are improved.With the splicing speed up to 25 frames per second for videos of 810×612 resolution, the 2D localization accuracy of our algorithm for a ground target can reach 7.87 m when the UAV flies at about 1000 m above ground.
参考文献

[1] 张岩,李建增,李德良,等.一种改进的无人机航空影像配准方法[J].计算机测量与控制,2015,23(6):2185-2187.

[2] LOWE D G.Distinctive image features from scale-invariant keypoints[J].International Journal of Computer Vision, 2004, 60(2):91-110.

[3] BAY H, TUYTELAARS T, VAN G L.SURF:speeded up robust features[C]//Proceedings of the 9th European Conference on Computer Vision, 2006:404-417.

[4] SMITH S M, BRADY J M.SUSAN—a new approach to low level image processing[J].International Journal of Computer Vision, 1997, 23(1):45-78.

[5] MAIR E, HAGER G D, BURSCHKA D, et al.Adaptive and generic corner detection based on the accelerated segment test[C]//Proceedings of the 11th European Conference on Computer Vision, 2010:183-196.

[6] LEUTENEGGER S, CHLI M, SIEGWART R Y.BRISK:binary robust invariant scalable keypoints[C]//Proceedings of International Conference on Computer Vision, IEEE, 2011:2548-2555.

[7] ALAHI A, ORTIZ R, VANDERGHEYNST P.FREAK:fast retina keypoint[C]//Proceedings of Computer Version and Pattern Recognition, IEEE, 2011:510-517.

[8] 张岩,李建增,李德良,等.基于特征的遥感图像匹配技术研究[J].无线电工程,2016,46(2):61-64.

[9] 尚明姝.一种基于改进SURF的图像配准方法[J].微电子学与计算机,2014,31(2):125-128.

[10] 张岩,李建增,李德良,等.基于POS与图像匹配的无人机目标定位方法研究[J].军械工程学院学报,2015,27(1):39-45.

[11] 任超峰.航空视频影像的正射影像制作关键技术研究[D].武汉:武汉大学,2014.

[12] 宫阿都,何孝莹,雷添杰,等.无控制点数据的无人机影像快速处理[J].地球信息科学学报,2010,12(2):254-259.

[13] WINDER S A J, BROWN M.Learning local image descriptors [C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, IEEE, 2007:1-8.

张岩, 李建增, 李德良, 杜玉龙. 基于景象匹配的无人机侦察视频快速配准方法[J]. 电光与控制, 2017, 24(5): 30. ZHANG Yan, LI Jian-zeng, LI De-liang, DU Yu-long. A Fast Registration Method for UAV Videos Based on Scene Matching[J]. Electronics Optics & Control, 2017, 24(5): 30.

本文已被 1 篇论文引用
被引统计数据来源于中国光学期刊网
引用该论文: TXT   |   EndNote

相关论文

加载中...

关于本站 Cookie 的使用提示

中国光学期刊网使用基于 cookie 的技术来更好地为您提供各项服务,点击此处了解我们的隐私策略。 如您需继续使用本网站,请您授权我们使用本地 cookie 来保存部分信息。
全站搜索
您最值得信赖的光电行业旗舰网络服务平台!