Bilateral Weighted Regression Ranking Model with Spatial-Temporal Correlation Filter for Visual Tracking

Abstract

Many discriminative correlation filter (DCF)-based methods have successfully leveraged the guidance for solving two problems (i.e., the boundary effect and temporal filtering degradation) as a model prior to visual tracking. Regardless of the specific content of the tracking algorithms, the intuitive motivation of these methods is to control the degeneration of the updating loss of the objective function with a structural framework. While these methods rely mostly on various explicit prior regularization items, they always ignore the loss from the data fidelity term. Therefore, we propose a bilateral weighted regression ranking model with a spatial-temporal correlation filter, namely, BWRR. Here, we resort to two procedures for solving the above problems. First, BWRR introduces a bilateral constraint into the data fidelity term to control the loss of rows and columns of the filter learning data term. The weighted matrices could impose an adaptive penalty for large data loss during the learning process to avoid the tracking offset problem and model degradation problem. Second, the data of the updated weighted matrices is not directly applied to the calculation of the filter during each iteration. Instead, a new weighted product matrix is obtained by ranking and numerical transformation for updating the filter. We show that the proposed model converts the original correlation filter regression problem into a regression-with-ranking problem, thus avoiding the problem of positive and negative sample imbalance. Overall, the BWRR model is approximated as a linear equality constraint problem, which is iteratively solved by the alternating direction method of multipliers(ADMM). Qualitative and quantitative evaluations demonstrate the effectiveness and superiority of our proposed method by extensive and quantitative experiments on the OTB, VOT, and UAV datasets.