针对目标跟踪中目标框发生偏移、消失等问题，于在线学习机制下提出一种基于几何模糊的跟踪检测学习的目标跟踪方法。以跟踪-检测-学习为框架，利用Lucas-Kanade 算法，获得目标的初步跟踪结果。运用几何模糊的匹配思想代替传统检测手法，有效校正跟踪偏移，避免误差累计。整合器比较跟踪、检测结果与上一帧结果的相似度，通过计算正负样本与检测子区域的归一化相关系数比求得置信度，得到目标的精准定位。其结果通过学习器进行在线学习，从而进行下一帧的跟踪。实验结果表明，将该检测思想应用于快速移动目标跟踪时，在背景相似度较高的条件下，表现出了良好的性能，与其他新的方法比较也有较高的定位精度。

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.