基于快速判别式尺度估计的核相关滤波方法

针对核相关滤波（KCF）算法无法对视频序列中目标尺度变化作出响应的问题，提出一种基于快速判别式多尺度估计的核相关滤波跟踪算法。首先，使用核相关滤波器来估计目标位置；然后，通过使用一组不同尺度的目标样本来在线学习快速判别式尺度滤波器；最后，在目标位置应用学习的尺度滤波器来获得目标尺寸的准确估计。选取Visual Tracker Benchmark视频序列集进行实验，并与基于判别式尺度空间跟踪（DSST）的KCF算法和传统KCF算法进行对比，结果表明，在目标尺度发生变化时，所提算法在跟踪精度上提高了2.2%至10.8%；并且在平均帧率上，所提算法比DSST的KCF算法提高了19.1%至68.5%，表明该算法对目标尺度变化有很强的适应能力和较高的实时性。

Kernelized correlation filtering method based on fast discriminative scale estimation

Focusing on the issue that the Kernelized Correlation Filter (KCF) can not respond to the target scale change, a KCF target tracking algorithm based on fast discriminative scale estimation was proposed. Firstly, the target position was estimated by KCF. Then, a fast discriminative scale filter was learned online by using a set of target samples with different scales. Finally, an accurate estimation of the target size was obtained by applying the learned scale filter at the target position. The experiments were conducted on Visual Tracker Benchmark video sequence sets, and comparison was performed with the KCF algorithm based on Discriminative Scale Space Tracking (DSST) and the traditional KCF algorithm. Experimental results show that the tracking accuracy of the proposed algorithm is 2.2% to 10.8% higher than that of two contrast algorithms when the target scale changes, and the average frame rate of the proposed algorithm is also 19.1% to 68.5% higher than that of KCF algorithm based on DSST. The proposed algorithm has strong adaptability and high real-time performance to target scale change.