  More information leads to better decisions and predictions, right? Confirming
this hypothesis, several studies concluded that the simultaneous use of optical
and thermal images leads to better predictions in crowd counting. However, the
way multimodal models extract enriched features from both modalities is not yet
fully understood. Since the use of multimodal data usually increases the
complexity, inference time, and memory requirements of the models, it is
relevant to examine the differences and advantages of multimodal compared to
monomodal models. In this work, all available multimodal datasets for crowd
counting are used to investigate the differences between monomodal and
multimodal models. To do so, we designed a monomodal architecture that
considers the current state of research on monomodal crowd counting. In
addition, several multimodal architectures have been developed using different
multimodal learning strategies. The key components of the monomodal
architecture are also used in the multimodal architectures to be able to answer
whether multimodal models perform better in crowd counting in general.
Surprisingly, no general answer to this question can be derived from the
existing datasets. We found that the existing datasets hold a bias toward
thermal images. This was determined by analyzing the relationship between the
brightness of optical images and crowd count as well as examining the
annotations made for each dataset. Since answering this question is important
for future real-world applications of crowd counting, this paper establishes
criteria for a potential dataset suitable for answering whether multimodal
models perform better in crowd counting in general.
