With the introduction of correlation filtering (CF), the performance of visual object tracking is significantly improved. Circular shifts collecting samples is a key component of the CF tracker, and it also causes negative boundary effects. Most trackers add spatial regularization to alleviate boundary effects well. However, these trackers ignore the effect of environmental changes on tracking performance, and the filter discriminates poorly in the background interference. Here, to break these limitations, we propose a new correlation filter model, namely Environmental Perception with Spatial Regularization Correlation Filter for Visual Tracking. Specifically, we use the Average Peak to Correlation Energy (APCE) and the response value error between the two frames together to perceive environmental changes, which adjusts the learning rate to make the template more adaptable to environmental changes. To enhance the discriminatory capability of the filter, we use real background information as negative samples to train the filter model. In addition, the introduction of the regular term destroys the closed solution of CF, and this problem can be effectively solved by the use of the alternating direction method of multipliers (ADMM). Extensive experimental evaluations on three large tracking benchmarks are performed, which demonstrate the good performance of the proposed method over some of the state-of-the-art trackers.

随着相关过滤（CF）的引入，视觉对象跟踪的性能得到显着提高。循环移位收集样本是 CF 跟踪器的关键组成部分，它也会导致负面的边界效应。大多数跟踪器添加空间正则化以很好地减轻边界效应。然而，这些跟踪器忽略了环境变化对跟踪性能的影响，并且滤波器在背景干扰中的辨别能力很差。在这里，为了打破这些限制，我们提出了一种新的相关滤波器模型，即用于视觉跟踪的带有空间正则化相关滤波器的环境感知。具体来说，我们将平均峰值相关能量（APCE）和两帧之间的响应值误差一起用于感知环境变化，调整学习率，使模板更适应环境变化。为了增强滤波器的判别能力，我们使用真实的背景信息作为负样本来训练滤波器模型。另外，正则项的引入破坏了CF的封闭解，这个问题可以通过使用乘法器交替方向法（ADMM）有效解决。对三个大型跟踪基准进行了广泛的实验评估，这证明了所提出的方法在一些最先进的跟踪器上的良好性能。正则项的引入破坏了CF的封闭解，这个问题可以通过使用乘法器交替方向法（ADMM）有效解决。对三个大型跟踪基准进行了广泛的实验评估，这证明了所提出的方法在一些最先进的跟踪器上的良好性能。正则项的引入破坏了CF的封闭解，这个问题可以通过使用乘法器交替方向法（ADMM）有效解决。对三个大型跟踪基准进行了广泛的实验评估，这证明了所提出的方法在一些最先进的跟踪器上的良好性能。