  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.
