Robust visual tracking using temporal regularization correlation filter with high-confidence strategy

Target tracking is an essential task in contemporary computer vision applications. However, its effectiveness is susceptible to model drift, due to the different appearances of targets, which often compromises tracking robustness and precision. In this paper, a universally applicable method based on correlation filters is introduced to mitigate model drift in complex scenarios. It employs temporal-confidence samples as a priori to guide the model update process and ensure its precision and consistency over a long period. An improved update mechanism based on the peak side-lobe to peak correlation energy (PSPCE) criterion is proposed, which selects high-confidence samples along the temporal dimension to update temporal-confidence samples. Extensive experiments on various benchmarks demonstrate that the proposed method achieves a competitive performance compared with the state-of-the-art methods. Especially when the target appearance changes significantly, our method is more robust and can achieve a balance between precision and speed. Specifically, on the object tracking benchmark (OTB-100) dataset, compared to the baseline, the tracking precision of our model improves by 8.8%, 8.8%, 5.1%, 5.6%, and 6.9% for background clutter, deformation, occlusion, rotation, and illumination variation, respectively. The results indicate that this proposed method can significantly enhance the robustness and precision of target tracking in dynamic and challenging environments, offering a reliable solution for applications such as real-time monitoring, autonomous driving, and precision guidance.