In this paper, we propose a strong spatio-temporal mechanism with correlation filters to solve multi-modality tracking tasks. First, we use the features of the previous four frames as spatio-temporal features, then aggregate the spatio-temporal features into the filters learning and positioning of the adjacent frame. Second, we enhance the temporal and spatial characteristics of the current frame filter by learning the previous four frame filters and spatial penalty. From the experimental results on the GTOT, VOT-TIR2019 and RGBT234 datasets, our strong spatio-temporal correlation filters has achieved excellent performance.

中文翻译：

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基于强时空的相关滤波器用于鲁棒 RGB-T 跟踪

在本文中，我们提出了一种具有相关滤波器的强大时空机制来解决多模态跟踪任务。首先，我们将前四帧的特征作为时空特征，然后将时空特征聚合到相邻帧的过滤器学习和定位中。其次，我们通过学习前四个帧过滤器和空间惩罚来增强当前帧过滤器的时间和空间特性。从 GTOT、VOT-TIR2019 和 RGBT234 数据集的实验结果来看，我们的强时空相关滤波器取得了优异的性能。