In order to realize fast and accurate detection of moving targets under complex dynamic background, a moving object detection method based on BRISK (binary robust invariant scalable keypoints) algorithm is proposed. Firstly, the image is divided into blocks, and the image blocks are filtered by using image entropy. Then, aiming at the problem of large number of mismatch in the process of feature matching, the k-nearest neighbor algorithm and Euclidean distance are used to perform feature matching. Finally, the improved sequential sampling consistency algorithm is used to refine the feature points and further completes the background motion compensation, and morphological processing is used to segment the moving target. Through the verification of multiple video images, the proposed algorithm removes 32.7% of the feature points based on the original BRISK algorithm and improves the matching efficiency by 75%. The proposed algorithm has faster processing speed than previous algorithms and strong anti-noise performance.