在图像局部特征匹配的过程中，考虑特征的运动统计信息可以有效地筛除错误匹配，但是目前基于网格的运动统计方法不具备良好的尺度不变性与旋转不变性。针对该问题，文中提出了一种基于动态窗口运动统计的特征匹配筛选算法。该算法首先基于图像特征点位置建立快速近似最近邻索引结构，然后利用该索引结构为匹配建立动态窗口邻域，最后在此邻域上进行运动统计，并依据运动统计得分进行正确匹配的筛选。在多个数据集上进行了文中算法与其他算法综合性能的对比，实验结果显示: 在尺度与旋转角度变化较大的情况下测量准确率与召回率时，文中算法相比于基于网格的算法优势明显; 在更一般场景下，文中算法的综合匹配效果也要明显优于其他几种经典的匹配筛选算法; 与此同时，文中算法具有良好的时间性能，可以应用于实时任务。

During the image local feature matching process，error matches will be eliminated effectively by conside-ring the motion statistics of features． However，the current grid-based method of motion statistics works poorly with zoom and rotation． To solve this problem，a screening method for feature matching based on dynamic window mo-tion statistics was proposed． Firstly，the algorithm builds a fast approximate nearest neighbor index structure based on the location of image feature points． Then it sets up the dynamic window and computes motion statistics． Fina-lly，it eliminates error matches with the score of motion statistics． The experimental results show that，compared with other methods，the proposed method has a significant advantage over the algorithm based on grid in predicating precision and recall rate when the scale and angle change greatly． And in more general scenarios，the overall matc-hing effect of this algorithm is better than other real-time matching methods． Meanwhile，this algorithm has good time performance and can be applied to real-time tasks．