To solve the problem of tracking drifts or fail, a robust objects tracking algorithm based on geometric blur is proposed within the framework of online learning. Under the tracking-detection-learning mechanism, Lucas-Kanade algorithm is used to obtain the rough tracking estimation of the target. Based on the idea of geometric blur matching instead of traditional detection methods, the tracking drift is efficiently corrected. Then integrator is designed to compare the similarities between the previous frame and the results of the tracker and the detector. Their confidences are obtained by calculating normalized correlative coefficients between positive and negative samples and the detected region. An online learning is then developed to use the current result to update the tracker and the detector. Experimental results show that when applied to the fact moving target tracking under the condition of high background similarity, the proposed method performs well and outperforms other state-of-the-art methods with higher position accuracy.