  In the field of crowd counting research, many recent deep learning based
methods have demonstrated robust capabilities for accurately estimating crowd
sizes. However, the enhancement in their performance often arises from an
increase in the complexity of the model structure. This paper discusses how to
construct high-performance crowd counting models using only simple structures.
We proposes the Fuss-Free Network (FFNet) that is characterized by its simple
and efficieny structure, consisting of only a backbone network and a
multi-scale feature fusion structure. The multi-scale feature fusion structure
is a simple structure consisting of three branches, each only equipped with a
focus transition module, and combines the features from these branches through
the concatenation operation. Our proposed crowd counting model is trained and
evaluated on four widely used public datasets, and it achieves accuracy that is
comparable to that of existing complex models. Furthermore, we conduct a
comprehensive evaluation by replacing the existing backbones of various models
such as FFNet and CCTrans with different networks, including MobileNet-v3,
ConvNeXt-Tiny, and Swin-Transformer-Small. The experimental results further
indicate that excellent crowd counting performance can be achieved with the
simplied structure proposed by us.
