Correlation filter-based trackers (CFTs) have recently shown remarkable performance in the field of visual object tracking. The advantage of these trackers originates from their ability to convert time-domain calculations into frequency domain calculations. However, a significant problem of these CFTs is that the model is insufficiently robust when the tracking scenarios are too complicated, meaning that the ideal tracking performance cannot be acquired. Recent work has attempted to resolve this problem by reducing the boundary effects from modeling the foreground and background of the object target effectively (e.g., CFLB, BACF, and CACF). Although these methods have demonstrated reasonable performance, they are often affected by occlusion, deformation, scale variation, and other challenging scenes. In this study, considering the relationship between the current frame and the previous frame of a moving object target in a time series, we propose a temporal regularization strategy to improve the BACF tracker (denoted as TRBACF), a typical representative of the aforementioned trackers. The TRBACF tracker can efficiently adjust the model to adapt the change of the tracking scenes, thereby enhancing its robustness and accuracy. Moreover, the objective function of our TRBACF tracker can be solved by an improved alternating direction method of multipliers, which can speed up the calculation in the Fourier domain. Extensive experimental results demonstrate that the proposed TRBACF tracker achieves competitive tracking performance compared with state-of-the-art trackers.

基于相关滤波器的跟踪器（CFT）最近在视觉对象跟踪领域表现出了卓越的性能。这些跟踪器的优势源自其将时域计算转换为频域计算的能力。但是，这些CFT的一个重要问题是，当跟踪场景过于复杂时，该模型的鲁棒性不足，这意味着无法获得理想的跟踪性能。最近的工作已经尝试通过减少有效地模拟对象目标的前景和背景（例如CFLB，BACF和CACF）的边界效应来解决此问题。尽管这些方法已显示出合理的性能，但它们通常会受到遮挡，变形，比例变化和其他具有挑战性的场景的影响。在这个研究中，考虑到时间序列中移动对象目标的当前帧和前一帧之间的关系，我们提出了一种时间正则化策略来改进BACF跟踪器（表示为TRBACF），它是上述跟踪器的典型代表。TRBACF跟踪器可以有效地调整模型以适应跟踪场景的变化，从而增强其鲁棒性和准确性。此外，我们的TRBACF跟踪器的目标功能可以通过改进的乘法器交替方向方法来解决，从而可以加快傅立叶域中的计算速度。大量的实验结果表明，与最新的跟踪器相比，拟议的TRBACF跟踪器可实现竞争性的跟踪性能。我们提出了一种时间正则化策略来改进BACF跟踪器（表示为TRBACF），它是上述跟踪器的典型代表。TRBACF跟踪器可以有效地调整模型以适应跟踪场景的变化，从而增强其鲁棒性和准确性。此外，我们的TRBACF跟踪器的目标功能可以通过改进的乘法器交替方向方法来解决，从而可以加快傅立叶域中的计算速度。大量的实验结果表明，与最先进的跟踪器相比，所提出的TRBACF跟踪器具有竞争性的跟踪性能。我们提出了一种时间正则化策略来改进BACF跟踪器（表示为TRBACF），它是上述跟踪器的典型代表。TRBACF跟踪器可以有效地调整模型以适应跟踪场景的变化，从而增强其鲁棒性和准确性。此外，我们的TRBACF跟踪器的目标功能可以通过改进的乘法器交替方向方法来解决，从而可以加快傅立叶域中的计算速度。大量的实验结果表明，与最先进的跟踪器相比，所提出的TRBACF跟踪器具有竞争性的跟踪性能。TRBACF跟踪器可以有效地调整模型以适应跟踪场景的变化，从而增强其鲁棒性和准确性。此外，我们的TRBACF跟踪器的目标功能可以通过改进的乘法器交替方向方法来解决，从而可以加快傅立叶域中的计算速度。大量的实验结果表明，与最先进的跟踪器相比，所提出的TRBACF跟踪器具有竞争性的跟踪性能。TRBACF跟踪器可以有效地调整模型以适应跟踪场景的变化，从而增强其鲁棒性和准确性。此外，我们的TRBACF跟踪器的目标功能可以通过改进的乘法器交替方向方法来解决，从而可以加快傅立叶域中的计算速度。大量的实验结果表明，与最先进的跟踪器相比，所提出的TRBACF跟踪器具有竞争性的跟踪性能。