TAFNet: A Three-Stream Adaptive Fusion Network for RGB-T Crowd Counting
This work addresses crowd counting for surveillance or public safety applications, presenting an incremental improvement over existing methods.
The paper tackles crowd counting using paired RGB and thermal images, achieving over 20% improvement in mean average error and root mean squared error compared to state-of-the-art methods.
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.