Abstract: In order to solve the problem of object drift caused by Kernelized Correlation Filter (KCF) tracking algorithm when scale changes, a Fast Scale Adaptive tracking of Correlation Filter (FSACF) was proposed. Firstly, a global gradient combination feature map based on salient color features was obtained by directly extracting features for the original frame image, reducing the effect of subsequent scale calculation on the performance. Secondly, the method of separating window was performed on the global feature map, adaptively selecting the scale and calculating the corresponding maximum response value. Finally, a defined confidence function was used to adaptively update the iterative template function, improving robustness of the model. Experimental result on video sets with different interference attributes show that compared with KCF algorithm, the accuracy of the FSACF algorithm by was improved 7.4 percentage points, and the success rate was increased by 12.8 percentage points; compared with the algorithm without global feature and separating window, the Frames Per Second was improved by 1.5 times. The experimental results show that the FSACF algorithm avoids the object drift when facing scale change with certain efficiency, and is superior to the comparison algorithms in accuracy and success rate.

Key words: object tracking, Kernelized Correlation Filter (KCF), scale adaptive, global feature, confidence function

摘要： 针对核相关滤波器（KCF）跟踪算法在面对尺度变化时产生的目标漂移问题，提出一种分离窗口快速尺度自适应目标跟踪算法——FSACF。首先，通过直接对原始帧图像进行特征提取得到基于显著性颜色特征的全局梯度组合特征图，以减小后续的尺度计算对性能的影响；其次，对全局特征图采用分离窗口法，自适应地选取尺度大小并计算对应的最大响应值；最后，采用定义的置信度函数自适应地更新迭代模板函数，提高模型的鲁棒性。通过带有不同干扰属性的视频集上进行实验，发现FSACF算法与KCF算法相比，在精度上提升7.4个百分点，成功率提高12.8个百分点；与未采用全局特征和分离窗口的算法对比，处理速度上提升1.5倍。实验结果表明，FSACF算法在尺度变化发生时能有效避免目标漂移的产生，同时具有一定的效率，并在精度与成功率上均优于对比算法。

关键词: 目标跟踪, 核相关滤波器, 尺度自适应, 全局特征, 置信度函数