Abstract: 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.