This paper focuses on integrating information from RGB and thermal infrared modalities to perform RGB-T object tracking in the correlation filter framework. Our baseline tracker is Staple (Sum of Template and Pixel-wise LEarners), which combines complementary cues in the correlation filter framework with high efficiency. Given the input RGB and thermal videos, we utilize the baseline tracker due to its high performance in both of accuracy and speed. Different from previous correlation filter-based methods, we perform the fusion tracking at both the pixel-fusion and decision-fusion levels. Our tracker is robust to the dataset challenges, and due to the efficiency of FFT, our tracker can maintain high efficiency with superior performance. Extensive experiments on the RGBT234 dataset have demonstrated the effectiveness of our work.

中文翻译：

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互补学习者增强的实时 RGB-T 跟踪

本文的重点是整合来自 RGB 和热红外模式的信息，以在相关滤波器框架中执行 RGB-T 对象跟踪。我们的基线跟踪器是 Staple (Sum of Template and Pixel-wise LEarners)，它结合了相关过滤器框架中的互补线索，并具有很高的效率。鉴于输入的 RGB 和热视频，我们使用基线跟踪器，因为它在准确性和速度方面都具有高性能。与以前基于相关滤波器的方法不同，我们在像素融合和决策融合级别执行融合跟踪。我们的跟踪器对数据集挑战具有鲁棒性，并且由于 FFT 的效率，我们的跟踪器可以保持高效率和卓越的性能。RGBT234 数据集上的大量实验证明了我们工作的有效性。