  Multi-modal crowd counting involves estimating crowd density from both visual
and thermal/depth images. This task is challenging due to the significant gap
between these distinct modalities. In this paper, we propose a novel approach
by introducing an auxiliary broker modality and on this basis frame the task as
a triple-modal learning problem. We devise a fusion-based method to generate
this broker modality, leveraging a non-diffusion, lightweight counterpart of
modern denoising diffusion-based fusion models. Additionally, we identify and
address the ghosting effect caused by direct cross-modal image fusion in
multi-modal crowd counting. Through extensive experimental evaluations on
popular multi-modal crowd-counting datasets, we demonstrate the effectiveness
of our method, which introduces only 4 million additional parameters, yet
achieves promising results. The code is available at
https://github.com/HenryCilence/Broker-Modality-Crowd-Counting.
