Fuss-Free Network: A Simplified and Efficient Neural Network for Crowd
  Counting
Lei Chen Xingen Gao
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 introduces the
Fuss-Free Network (FFNet), a crowd counting deep learning model that is
characterized by its simplicity and efficiency in terms of its structure. The
model comprises only a backbone of a neural network and a multi-scale feature
fusion structure.The multi-scale feature fusion structure is a simple
architecture 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.The experimental results further
indicate that excellent performance in crowd counting tasks can also be
achieved by utilizing a simple, low-parameter, and computationally efficient
neural network structure.