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.
@article{arxiv.2202.08517,
title = {TAFNet: A Three-Stream Adaptive Fusion Network for RGB-T Crowd Counting},
author = {Haihan Tang and Yi Wang and Lap-Pui Chau},
journal= {arXiv preprint arXiv:2202.08517},
year = {2022}
}
Comments
This work has been accepted by IEEE International Symposium on Circuits and Systems (ISCAS) 2022