Fuss-Free Network: A Simplified and Efficient Neural Network for Crowd Counting

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.