目的 如何对目标进行快速鲁棒的跟踪一直是计算机视觉的重要研究方向之一，TLD（tracking-learning-detection）算法为这一问题提供了一种有效的解决方法，为了进一步提高TLD算法的跟踪性能，从两个方面对其进行了改进。方法 首先在跟踪模块采用尺度自适应的核相关滤波器（KCF）作为跟踪器，考虑到跟踪模块与检测模块相互独立，本文算法使用检测模块对跟踪模块结果的准确性进行判断，并根据判断结果对KCF滤波器模板进行有选择地更新；然后在检测模块，运用光流法对目标位置进行初步预测，依据预测结果动态调整目标检测区域后，再使用分类器对目标进行精确定位。结果 为了验证本文算法的优越性，对其进行了两组实验，实验1在OTB2013和Temple Color128这两个平台上对本文算法进行了跟踪性能的测试，其结果表明本文算法在OTB2013上的跟踪精度和成功率分别为0.761和0.559，在Temple Color128上的跟踪精度和成功率分别为0.678和0.481，且在所有测试视频上的平均跟踪速度达到了27.92帧/s；实验2将本文算法与其他3种改进算法在随机选取的8组视频上进行了跟踪测试与对比分析，实验结果表明，本文算法具有最小的中心位置误差14.01、最大的重叠率72.2%以及最快的跟踪速度26.23帧/s，展现出良好的跟踪性能。结论 本文算法使用KCF跟踪器，提高了算法对遮挡、光照变化和运动模糊等场景的适应能力，使用光流法缩小检测区域，提高了算法的跟踪速度。实验结果表明，本文算法在多数情况下均取得优于参考算法的跟踪性能，在对目标进行长时间跟踪时表现出良好的跟踪鲁棒性。

Objective Visual tracking is widely applied in fields, such as video surveillance, human-computer interaction, and intelligent transportation, at present. In recent years, domestic and foreign researchers have proposed numerous tracking algorithms for this purpose. When applied to practical use, these algorithms are required to track a target extensively. However, continuously tracking a target is difficult for most algorithms given the complexity of the tracking scenario. Therefore, conducting rapid and robust tracking of a target is a key issue that must be solved when applying visual target tracking technology to practical use. TLD algorithm provides an effective solution to this issue. This study improves two aspects of the TLD algorithm to improve its tracking performance. Method First, a scale adaptive kernel correlation filter (KCF) is used as a tracker in the tracking module. The KCF algorithm cannot adapt to the scale change of the target because the size of the filter template is fixed. However, the detection module of the TLD algorithm has a certain scale adaptability. Therefore, the proposed algorithm utilizes the scale adaptive capabilities of the detection module to measure the scale of the region of interest of the KCF tracker. Moreover, the scale adjustments can enable the KCF tracker to achieve an improved tracking precision. The algorithm uses the detection module to assess the accuracy of the results of the tracking, module and selectively updates the KCF filter template in accordance with the assessed result because the tracking and detection modules are independent of each other. Second, an optical flow method in the detection module is used to preliminarily predict a target position. The optical flow method is used to estimate the target movement between two adjacent frames without any prior knowledge. The target detection area is set in accordance with the predicted position, and the size of the detection area is proportional to the target size. A three-layer cascade classifier is used to locate the target accurately after dynamically adjusting the target detection area. An anti-interference capability of the algorithm to similar objects in the scene is enhanced since the target motion information is introduced. Result Two sets of experiments are conducted to verify the superiority of the proposed algorithm. The first set of experiments is conducted on the OTB2013 and Temple Color 128 data platforms. The OTB2013 data platform has 50 sets of video sequences, and the Temple Color 128 data platform has 128 sets of video sequences. Results show that the tracking accuracy and success rate of the algorithm on the OTB2013 data platform are 0.761 and 0.559, respectively, and the tracking accuracy and success rate of the algorithm on the Temple Color 128 data platform are 0.678 and 0.481, correspondingly. The proposed algorithm is compared with six state-of-the art algorithms, namely, DSST, KCF, CNT, Struck, TLD, and DLT. Among all the algorithms, the proposed algorithm exhibits the optimum performance on the two data platforms,Besides, the. The average tracking speed of all test videos reaches 27.92 frame/s, thereby indicating a favorable real-time performance. In another set of experiments, the proposed algorithm and three other improved algorithms are tested and compared with the randomly selected eight sets of video sequences. The experimental results show that the proposed algorithm has the smallest center position error of 14.01, the largest overlap rate of 72.2%, and the fastest tracking speed of 26.23 frame/s, thus denoting that the proposed algorithm achieves the optimum tracking performance among all of the improved algorithms. Conclusion The proposed algorithm uses the KCF tracker to improve the capability of the algorithm to adapt to different scenes, such as occlusion, illumination change, and motion blur. Furthermore, the proposed algorithm uses the optical flow method to narrow the detection area. Consequently, the tracking speed of the algorithm is improved. The experimental results show that the proposed algorithm exhibits better tracking performance than the reference algorithm in most cases and achieves favorable tracking robustness in an extensive tracking process.