To ensure the performance of small moving target detection in dynamic backgrounds based on depth optical flow estimation,fewer times of down sampling are generally adopted to maintain a high resolution,which leads to large computational time consumption.Feature matching is a core processing link of depth optical flow estimation,which takes up a large proportion of the overall time consumption of optical flow estimation,and is very sensitive to the operation times of down sampling.Therefore,a fast optical flow estimation algorithm based on local feature matching is proposed.The target motion information is introduced,the spatial range of feature matching is narrowed,and the amount of data to be processed is reduced.A block-based local matching strategy is designed,and the batch processing mechanism is introduced to avoid the problem of large time consumption in data processing of the pointwise local matching strategythus to accelerate the algorithm.Based on this,CenterNet network is adopted to detect the optical flow anomaly areas corresponding to the moving target in the optical flow field obtained by optical flow estimation.Experimental verification is conducted from the perspectives of optical flow estimation time consumption and detection accuracy.The results show that as for small moving target detection,the block-based feature matching optical flow estimation has about 25% less time consumption than the global feature matching optical flow estimation,while the target detection performance is roughly equivalent.