为解决光流估计网络在目标边缘分割、运动速度和运动方向不准确的问题，提出基于视频帧间局部相关信息的光流估计网络。运用特征编码器从图像中提取出编码特征，通过上下文网络获取图像的上下文特征。采用下采样处理减小特征尺寸提高计算效率。根据连续两帧光流图像位移较小的特性，提出一种分区计算视觉相似度的方法，构建更为精细的4D相关体。采用残差滤波器和相似卷积块的方法，分别针对相关体和光流信息进行操作，更有效地保留局部微小位移信息。研究结果表明：采用基于视频帧间局部相关信息的光流估计网络进行计算，端点误差分别实现了8.0%和5.7%的优化，显著提升了光流估计的准确性，对复杂场景下光流信息提取更准确。研究结果可为自动驾驶、智能安防等领域提供参考。

To address the challenges associated with inaccurate target edge segmentation, motion speed, and motion direction, this paper introduces an optical flow estimation network that leverages local correlation information between video frames. Initially, the network employs a feature encoder to extract encoding features from the image and capture contextual information through a context network. Subsequently, the feature size is reduced through downsampling to enhance computational efficiency. Given the minute displacement of the optical flow image across consecutive frames, a partition-based visual similarity computation method is proposed to construct a more refined 4D correlation volume. Residual filters and similar convolution blocks are utilized for processing the correlation volume and optical flow information, respectively, ensuring the preservation of local small displacement details. The research results show that the optical flow estimation network based on the local correlation information between video frames has achieved optimizations of 8.0% and 5.7% respectively in the optical flow estimation evaluation metric (endpoint error, EPE). This significantly improves the accuracy of optical flow estimation and effectively alleviates the problem of inaccurate optical flow information extraction in complex scenarios. The research conclusions provide references for fields such as autonomous driving and intelligent security.