Kernel correlation filter (KCF) algorithm is famous for fast tracking speed and has been used widely, while it is susceptible under some challenging scenes. Aiming at the problem of limited applicable scenes of KCF, an improved target tracking method based on extraction of corner points is proposed. Eight neighborhood template is used to filter corner points, and adaptive threshold approach is introduced to minimize the influence of noise on corner extraction. Moreover, owing to appropriate quantity and strong robustness, adaptive corners can reduce the influence of incomplete extraction on edge points while enhancing extraction speed. Subsequently, histogram of oriented gradient (HOG) and color name (CN) features are extracted, and targets are segmented into blocks to solve the problem that standard KCF is likely to lose targets when they have deformation and scale variation. Under occlusion, learning rate parameter is adjusted adaptively ensuring accuracy of model updating. To get rid of drift phenomenon when targets have fast motion, multiple targets are correlated, and the final center position of each target is achieved according to the contour depicted by generalized Hough algorithm. Experimental results on the dataset and actual scenes demonstrate that our proposed method improves the EAO from 0.299 to 0.505, and improves overall precision of diverse attributes from 0.781 to 0.787.

核相关滤波器（KCF）算法以跟踪速度快而被广泛使用，但在一些具有挑战性的场景下很容易受到影响。针对KCF适用场景有限的问题，提出一种基于角点提取的改进目标跟踪方法。采用八邻域模板来过滤角点，并引入自适应阈值方法来最小化噪声对角点提取的影响。此外，自适应角点由于数量合适、鲁棒性强，可以在提高提取速度的同时，减少不完全提取对边缘点的影响。随后，提取方向梯度直方图（HOG）和颜色名称（CN）特征，并将目标分割成块，解决标准KCF在发生变形和尺度变化时容易丢失目标的问题。在遮挡情况下，自适应调整学习率参数，保证模型更新的准确性。为了消除目标快速运动时的漂移现象，将多个目标进行关联，根据广义Hough算法描绘的轮廓得到每个目标的最终中心位置。数据集和实际场景的实验结果表明，我们提出的方法将EAO从0.299提高到0.505，并将各种属性的整体精度从0.781提高到0.787。