MGSNet: A multi-scale and gated spatial attention network for crowd counting

Recently, crowd counting via estimating a density map has been widely studied. However, it still has a variety of issues to overcome, such as large-scale variation of population, complex background noise, perspective distortion, etc. The large-scale variation of heads will restrict the performance of crowd counting approaches, and the complex background noise will result in the background, such as leaf and mesh, being incorrectly recognized as heads. To maintain large-scale variation and generate a high-quality estimated density map, we propose a novel multi-scale fusion scale-aware attention network called multi-scale and gated spatial attention network (MGSNet). In MGSNet, the first 10 layers of VGG16 with Batch Normalization (BN) are utilized as backbone. Then, two branches, i.e., a large-scale branch and a scale-aware attention branch, are followed. The large-scale branch is used to overcome the large-scale variation of heads in crowd images, in which a Scale Information Aggregation Block (SIAB) is employed to extract multi-scale features by utilizing dilated convolution with different receptive fields. The scale-aware attention branch is used to address complex background noise in crowd scenes, in which a Gated Spatial Attention Block (GSAB) inspired by the Long Short-term Memory Networks (LSTM) is employed to fuse the previous information with different scales and retain the appropriate scale information of crowds. We demonstrate our proposed method on the ShanghaiTech (Part AB), UCF-CC-50 and UCF-QNRF datasets. The experimental results show its effectiveness over the state-of-the-art.

Automated summary

This paper proposes a multi-scale and gated spatial attention network (MGSNet) for crowd counting, which aims to address the challenges of large-scale variation and complex background noise. Experimental results demonstrate its effectiveness over state-of-the-art approaches on multiple datasets.