The RGB-T trackers based on correlation filter framework have been extensively investigated for that they can track targets more accurately in most complex scenes. However, the performance of these trackers is limited when facing some specific challenging scenarios, such as occlusion and background clutter. For different tracking targets, most of these trackers utilize fixed regularization constraint to build the filter model, which is obviously unreasonable to effectively present the appearance changes and characteristics of a specific target. In addition, they adopt a simple model update mechanism based on linear interpolation, which can easily lead to model degradation in challenging scenarios, resulting in tracker drift. To solve the above problems, we propose a novel adaptive spatial-temporal regularized correlation filter model to learn an appropriate regularization for achieving robust tracking and a relative peak discriminative method for model updating to avoid the model degradation. Besides, to make better integrate the unique advantages of the two modes and adapt the changing appearance of the target, an adaptive weighting ensemble scheme and a multi-scale search mechanism are adopted, respectively. To optimize the proposed model, we designed an efficient ADMM algorithm, which greatly improved the efficiency. Extensive experiments have been carried out on two available datasets, RGBT234 and RGBT210, and the experimental results indicate that the tracker proposed by us performs favorably in both accuracy and robustness against the state-of-the-art RGB-T trackers.

基于相关滤波器框架的RGB-T跟踪器已被广泛研究，因为它们可以在大多数复杂场景中更精确地跟踪目标。但是，当遇到某些特定的挑战性场景（例如遮挡和背景混乱）时，这些跟踪器的性能会受到限制。对于不同的跟踪目标，大多数这些跟踪器利用固定的正则化约束条件来构建过滤器模型，这对于有效地呈现特定目标的外观变化和特征显然是不合理的。此外，他们采用基于线性插值的简单模型更新机制，这很容易在挑战性场景中导致模型退化，从而导致跟踪器漂移。为了解决上述问题，我们提出了一种新颖的自适应时空正则化相关滤波器模型，以学习用于实现鲁棒跟踪的适当正则化以及用于模型更新以避免模型降级的相对峰值判别方法。此外，为了更好地融合两种模式的独特优势并适应目标的变化外观，分别采用了自适应加权集成方案和多尺度搜索机制。为了优化提出的模型，我们设计了一种有效的ADMM算法，大大提高了效率。在两个可用的数据集RGBT234和RGBT210上进行了广泛的实验，实验结果表明，我们提出的跟踪器在精度和鲁棒性方面均优于最新的RGB-T跟踪器。