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Three-Dimensional Registration Method of KLT Inter-Frame Tracking and Improved ORB Feature Detection in Augmented Reality

机译:增强现实中KLT帧间跟踪的三维配准方法和改进的ORB特征检测

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Due to the problems of poor real-time performance and unstable registration of visual-based augmented reality 3D registration technology, a 3D registration method based on KLT inter-frame tracking and improved ORB feature detection and matching is proposed in this paper. The registration process in our system is directly divided into offline stage and online stage. In the offline stage, the template image is trained with the improved ORB algorithm to generate a subset of feature description. In the online stage, I match firstly the global video frame with the template features, and then optimize the matching results through the RANSAC algorithm, ultimately through the feature matching threshold to determine whether there is a target in the video frames. If there is a target in the scence, the KLT tracking algorithm is used to track the target. By solving the homography matrix between the video frames, the homography matrix relative to the template is obtained at any time. The homography matrix obtained by solving uses the projection imaging theory and the PNP method to complete the calculation of the three-dimensional registration matrix. In addition, for KLT, it is easy to track the drift, and it is analyzed by establishing a threshold, and if the tracking fails, the target is detected again. Experiments show that the three-dimensional registration method proposed in this paper can work normally under the condition of target rotation and partial occlusion. The real-time FPS reaches more than 15fps, and the registration stability is higher than the KCF + ORB three-dimensional registration method.
机译:针对基于视觉的增强现实3D配准技术实时性差,配准不稳定等问题,提出了一种基于KLT帧间跟踪和改进的ORB特征检测与匹配的3D配准方法。我们系统中的注册过程直接分为离线阶段和在线阶段。在离线阶段,使用改进的ORB算法训练模板图像,以生成特征描述的子集。在在线阶段,我首先将全局视频帧与模板特征进行匹配,然后通过RANSAC算法优化匹配结果,最终通过特征匹配阈值确定视频帧中是否存在目标。如果场景中有目标,则使用KLT跟踪算法来跟踪目标。通过求解视频帧之间的单应性矩阵,可以随时获取相对于模板的单应性矩阵。通过求解获得的单应性矩阵使用投影成像理论和PNP方法完成了三维配准矩阵的计算。另外,对于KLT,很容易跟踪漂移,并通过建立阈值对其进行分析,如果跟踪失败,则会再次检测目标。实验表明,本文提出的三维配准方法可以在目标旋转和部分遮挡的情况下正常工作。实时FPS达到15fps以上,并且配准稳定性高于KCF + ORB三维配准方法。

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