Scale Adaptive Kernel Correlation Filtering for Target Tracking

Focusing on the issue that the traditional tracking method is difficult to adapt to the target scale variation in real time accurately, an adaptive scale target tracking algorithm based on kernel correlation filtering tracking framework, which adapts a scale estimation method, is proposed. Firstly, the regularized least squares classifier is used to obtain the filter template, and the position of the target is estimated by detecting the candidate samples. Then, the scale of current frame is determined based on the target size of the previous frame, and the scale samples arc obtained by the maximum response value through the scale estimation method. Finally, the target and scale model parameters arc updated online according to the occlusion detection mechanism. The experimental results show that the proposed algorithm improves the distance precision by 17.12% and the success rate by 10.77% as compared with the best of the other tracking algorithms. In complex scenes, such as background clutters, severe occlusion, and illumination, posture and scale variation, the proposed algorithm still has a good tracking performance.