This paper proposes a new method based on sparse optical flow to address the problem of target extraction and tracking in dynamic backgrounds. First

the pyramid of the LK optical flow method is used to generate an optical flow image to match the feature points between two images. Second

the feature points are divided preliminarily based on the optical flow information on the displacement and direction of the optical flow image. Third

the center iteration method is applied to remove the noise feature points that do not belong to the target motion area. Fourth

the maximum intersection of the target feature points in the first frames leads to the stable target points that are tracked in the subsequent frames. In the case of blocked targets in subsequent frames

we apply the Kalman estimation method and introduce a blocked coefficient related to feature points to predict the target location and locate the target quickly upon its reappearance. The experimental results prove the capability of the proposed algorithm to accurately locate the target. The false detection rate of the target feature points is reduced by 10%

and the tracking rate reaches as high as 97% even when the target is blocked. The proposed method demonstrates excellent performance in meeting real-time requirements in dynamic backgrounds and can be applied to tracking slow- or fast-moving targets in blocked or unobstructed scenes.