  Convolutional Neural Network (CNN) based crowd counting methods have achieved
promising results in the past few years. However, the scale variation problem
is still a huge challenge for accurate count estimation. In this paper, we
propose a multi-scale feature aggregation network (MSFANet) that can alleviate
this problem to some extent. Specifically, our approach consists of two feature
aggregation modules: the short aggregation (ShortAgg) and the skip aggregation
(SkipAgg). The ShortAgg module aggregates the features of the adjacent
convolution blocks. Its purpose is to make features with different receptive
fields fused gradually from the bottom to the top of the network. The SkipAgg
module directly propagates features with small receptive fields to features
with much larger receptive fields. Its purpose is to promote the fusion of
features with small and large receptive fields. Especially, the SkipAgg module
introduces the local self-attention features from the Swin Transformer blocks
to incorporate rich spatial information. Furthermore, we present a
local-and-global based counting loss by considering the non-uniform crowd
distribution. Extensive experiments on four challenging datasets (ShanghaiTech
dataset, UCF_CC_50 dataset, UCF-QNRF Dataset, WorldExpo'10 dataset) demonstrate
the proposed easy-to-implement MSFANet can achieve promising results when
compared with the previous state-of-the-art approaches.
