Feature tracking plays a vital role in a monocular visual-inertial system (VINS) or a visual task based on feature points. However, in terms of feature points tracking, most of the existing VINS solutions adopt the classical method where the feature extraction and matching are carried out independently. Due to such nonintegrated working manner, the matching performs traversal operation globally rather than in a reasonable search space, which increases the probability of false matches, and reduces the accuracy. In this article, a novel feature points tracking algorithm in terms of inertial measurement unit (IMU)-aided information fusion is presented, which can reduce the search space to improve accuracy, and boost efficiency. This method starts with a preintegration-based predictor which can predict the position of the feature points in the current frame according to the feature points that need to be matched in the previous frame, and the measurements of IMU between two frames. Then, a variable-sized search window, in which the feature extraction and matching are locally carried out, is built at the predicted location. Furthermore, to solve the convergence and overlap problem of feature points in the tracking process, a feature update module of the bionic population is attached to the local matcher. Finally, the comparison experiments are performed on the public datasets to show the effectiveness and superiority of our method. It should be emphasized that the proposed algorithm is a universal framework of solution, which can meet various task requirements by choosing different feature points extraction algorithms, and improve the efficiency of matching in an integrated way.