尺度估计和多特征融合的目标跟踪算法研究

针对核相关滤波器跟踪算法（Kernel Correlation Filter，KCF）在特征提取单一以及尺度估计不足而导致跟踪效果不佳的问题，本文提出了一种多特征融合的尺度自适应核相关滤波目标跟踪算法。首先，使用帧差法将相邻帧图像对应像素值相减得到差分图像；其次，对差分图像提取方向直方图特征，再与目标的均一局部二值纹理特征和颜色特征进行线性加权融合；最后，引入一种尺度估计策略，将尺度滤波器的估计值与分块算法的估计值融合计算得出目标的尺度和位置。实验数据表明，本方法能有效的改善核相关滤波器的跟踪性能，且与其他主流算法相比，在尺度变换下也有较好的跟踪效果。 

Research on target tracking algorithm based on scale estimation and multi-feature fusion

Aiming at the problem of single feature extraction and insufficient scale estimation of kernel correlation filter tracking algorithm, which leads to poor tracking effect, this paper proposed a multi-feature fusion and scale adaptive kernel correlation filter target tracking algorithm. First, the frame difference method was used to subtract the corresponding pixel values of adjacent frame images to obtain differential image features; secondly, direction histogram feature extraction was performed on the difference image, and then linear weighted and fused with the uniform local binary pattern features and color names features. Finally, an adaptive scale estimation strategy was proposed, which combined the estimated value of the scale filter and the block algorithm to calculate the scale and position of the target. Our method can improve the tracking performance of the kernel correlation filter, and compared with other mainstream algorithms, it also has better tracking effect under scale transformation.