目的 目标遮挡一直是限制跟踪算法精确度和稳定性的问题之一，针对该问题，提出一种抗遮挡的多层次重定位目标跟踪算法。方法 通过平均峰值相关能量动态分配特征权重，将梯度特征与颜色直方图特征动态地结合起来进行目标跟踪。利用多峰值检测和峰值波动情况进行目标状态判定，若目标状态不理想，则停止模板更新，避免逐帧更新导致目标漂移，继续跟踪目标；若判定目标遮挡，则提取对应特征点，使用最邻近距离比进行特征匹配和筛选，丢弃负样本的最邻近样本作为二次筛选，利用广义霍夫变换进行第3次筛选并重定位目标，对目标继续跟踪。结果 在标准数据集OTB（object tracking benchmark）100和LaSOT（large-scale single object tracking）上的实验结果显示，本文算法的精确率分别为0.885和0.301，相较于Staple算法分别提升了13.5%和30.3%。结论 在目标发生遮挡的场景中，本文方法能够重定位目标并且继续跟踪，优化后的模板更新策略提高了算法速度。目标状态的判定有效估计了目标遮挡问题，可以及时采取应对策略，提高算法在复杂环境下的稳定性。

Objective As one of the important research directions in the field of computer vision, target tracking has a wide range of applications in the fields of video surveillance, human computer interaction, and behavior analysis. A tracking algorithm analyzes target location information in real time in a subsequent sequence of video images by giving the target information (i.e., location and size) in the first frame. At present, target tracking technology has achieved considerable progress, but the robustness of real-time tracking algorithms is still affected by factors, such as target occlusion, illumination change, scale change, fast motion, and background interference. Among these issues, the occlusion problem is the most prominent. A complementary learning correlation filter tracking algorithm updates the template frame by frame. The reliability of the sample is not discriminated during template update, and the sample is not filtered. When background information is complex, particularly when the target is occluded, the template update result will gradually deviate from the target to be tracked. In particular, the color feature is more susceptible to complex environmental factors, aggravating target drift, and thus, template update leads to target drift and occlusion. Method The problem of losing the target persists. The occlusion problem has always limited the accuracy and stability of tracking algorithms. To address this problem, an anti-occlusion multilevel retargeting target tracking algorithm is proposed. This algorithm has three innovations.1) By using the average peak correlation energy, the gradient and color histogram features are dynamically combined to distribute weight reasonably. 2) The target state is determined in real time through peak responses and fluctuations, and the template update strategy is optimized. 3) To address the occlusion problem during the tracking process, a multilevel target relocation strategy is proposed and multilevel filtered feature points are used in the target relocation operation. Feature weight is determined on the basis of the dynamically changing average peak correlation energy, and it is used to combine the gradient and color histogram features for target tracking. After the current frame identifies the target position, target state determination is performed using multi-peak detection and the peak fluctuation condition. If the target state is not ideal, then template update is stopped. Frame-by-frame update is avoided, causing the target to drift, and then target tracking is continued. If target occlusion is determined, then the oriented fast and rotated brief feature of the target is extracted. The nearest neighbor distance ratio of the feature points is matched and filtered, and the nearest neighbor of the negative sample is discarded as secondary screening. Third screening is performed via the generalized Hough transform, the target is relocated, and tracking the target is continued. Result To objectively verify the advantages and disadvantages of the proposed algorithm, 10 groups of image sequences, namely, Basketball, Bird2, CarDark, CarScale, DragonBaby, Girl, Human5, Human8, Singer1, and Walking2, are selected. Nine algorithms, including the proposed algorithm, are selected for the tracking experiments. The eight other algorithms are as follows: kernel correlation filters, discriminative scale space tracking, staple, background-aware correlation filter, spatially regularized correlation filter, scale adaptive multiple features, efficient convolution operators, and spatiotemporal regularized correlation filter. Experimental results for the standard datasets OTB(object tracking benchmark)100 and LaSOT(large-scale single object tracking) show that the accuracy of the algorithm proposed in this study is 0.885 and 0.301, which are 13.5% and 30.3% higher than the original algorithm, respectively. Conclusion In the scenario where in the target is occluded, the target can be repositioned and tracking continues. The optimized template update strategy increases the speed of the algorithm. The determination of the target state effectively estimates the target occlusion problem and can adopt a timely coping strategy to improve the stability of the algorithm in a complex environment.