Aiming at the problem of real-time augmented reality tracking registration of motion blurred template matching image construction model, this paper proposes a template matching target tracking algorithm based on improved efficient second-order minimization (ESM). Based on ESM algorithm, an improved efficient second-order minimization algorithm is used to track the motion template. Firstly, the image acquisition and noise reduction preprocessing are carried out, and the image acquisition model of high-speed moving target and the edge feature detection algorithm of moving feature points are designed. The gray-scale histogram denoising method is used to suppress the edge fuzzy set of dynamic feature points in the image of high-speed moving target. The feature extraction and matching are modeled, and the selection of feature points and the search algorithm are optimized. Then the block detection method is used to reconstruct the region of high-speed moving target, and the edge contour detection and feature point matching of the moving target are carried out in the affine invariant region. The image block template matching method is used to realize the real-time augmented reality tracking registration of high-speed moving target. The simulation results show that the proposed method has good effect and superior performance in real-time augmented reality tracking registration of moving targets.

针对运动模糊模板匹配图像构建模型的实时增强现实跟踪配准问题，提出一种基于改进高效二阶最小化(ESM)的模板匹配目标跟踪算法。在ESM算法的基础上，采用改进的高效二阶最小化算法跟踪运动模板。首先进行图像采集和降噪预处理,设计了高速运动目标图像采集模型和运动特征点边缘特征检测算法。采用灰度直方图去噪方法抑制高速运动目标图像中动态特征点的边缘模糊集。对特征提取和匹配进行建模，并对特征点的选择和搜索算法进行了优化。然后采用块检测方法重构高速运动目标区域，在仿射不变区域进行运动目标的边缘轮廓检测和特征点匹配。采用图像块模板匹配方法实现高速运动目标的实时增强现实跟踪配准。仿真结果表明，该方法在运动目标的实时增强现实跟踪配准中具有良好的效果和优越的性能。采用图像块模板匹配方法实现高速运动目标的实时增强现实跟踪配准。仿真结果表明，该方法在运动目标的实时增强现实跟踪配准中具有良好的效果和优越的性能。采用图像块模板匹配方法实现高速运动目标的实时增强现实跟踪配准。仿真结果表明，该方法在运动目标的实时增强现实跟踪配准中具有良好的效果和优越的性能。