论文标题
TAFNET：用于RGB-T人群计数的三潮自适应融合网络
TAFNet: A Three-Stream Adaptive Fusion Network for RGB-T Crowd Counting
论文作者
论文摘要
在本文中，我们提出了一个名为TAFNET的三潮自适应融合网络，该网络使用配对的RGB和热图像进行人群计数。具体而言，Tafnet分为一个主流和两个辅助流。我们结合了一对RGB和热图像，以构成主流的输入。两个辅助流分别利用RGB图像和热图像来提取特定于模态特征。此外，我们提出了一个信息改进模块（IIM），以适应特定于模态特征。 RGBT-CC数据集的实验结果表明，与最新方法相比，我们的方法在平均平均误差和均方根误差方面取得了20％以上的提高。源代码将在https://github.com/tanghaihan/tafnet上公开获取。
In this paper, we propose a three-stream adaptive fusion network named TAFNet, which uses paired RGB and thermal images for crowd counting. Specifically, TAFNet is divided into one main stream and two auxiliary streams. We combine a pair of RGB and thermal images to constitute the input of main stream. Two auxiliary streams respectively exploit RGB image and thermal image to extract modality-specific features. Besides, we propose an Information Improvement Module (IIM) to fuse the modality-specific features into the main stream adaptively. Experiment results on RGBT-CC dataset show that our method achieves more than 20% improvement on mean average error and root mean squared error compared with state-of-the-art method. The source code will be publicly available at https://github.com/TANGHAIHAN/TAFNet.
