Abstract: The correlation filter target tracking algorithm kernelized correlation filters (KCF) utilizes a single feature
to describe the target, which cannot accurately track the target in complex environments, or when the target scale
changes significantly. To address these problems, a scale adaptive target tracking algorithm based on depth estimation
and feature fusion was proposed. Firstly, the depth of the target in the video was estimated using the depth estimation
network, and the depth-scale estimation model was built and trained. In the tracking, the histogram of oriented
gradient (HOG) features and color name (CN) features of the target were fused to train the correlation filter, and the
depth of the target was obtained using the depth estimation network. In addition, the depth-scale estimation model was
employed to obtain the scale of the target. Thus, the target size can be adjusted when the target scale changes, and the
scale adaptive target tracking algorithm is realized. The experimental results show that compared with the classical
algorithm KCF, higher accuracy can be obtained, and compared with the scale adaptive algorithm discriminative scale space tracking (DSST), the tracking speed is faster when the scale changes greatly, and the robustness is better in
cases of complex environments and obscured targets.

Key words: target tracking, correlation filter, feature fusion, depth estimation network, depth-scale estimation model;
scale adaptation