  RGB-Thermal (RGB-T) crowd counting is a challenging task, which uses thermal
images as complementary information to RGB images to deal with the decreased
performance of unimodal RGB-based methods in scenes with low-illumination or
similar backgrounds. Most existing methods propose well-designed structures for
cross-modal fusion in RGB-T crowd counting. However, these methods have
difficulty in encoding cross-modal contextual semantic information in RGB-T
image pairs. Considering the aforementioned problem, we propose a two-stream
RGB-T crowd counting network called Multi-Attention Fusion Network (MAFNet),
which aims to fully capture long-range contextual information from the RGB and
thermal modalities based on the attention mechanism. Specifically, in the
encoder part, a Multi-Attention Fusion (MAF) module is embedded into different
stages of the two modality-specific branches for cross-modal fusion at the
global level. In addition, a Multi-modal Multi-scale Aggregation (MMA)
regression head is introduced to make full use of the multi-scale and
contextual information across modalities to generate high-quality crowd density
maps. Extensive experiments on two popular datasets show that the proposed
MAFNet is effective for RGB-T crowd counting and achieves the state-of-the-art
performance.
