HTNet: A Hybrid Model Boosted by Triple Self-attention for Crowd Counting

The swift development of convolutional neural network (CNN) has enabled significant headway in crowd counting research. However, the fixed-size convolutional kernels of traditional methods make it difficult to handle problems such as drastic scale change and complex background interference. In this regard, we propose a hybrid crowd counting model to tackle existing challenges. Firstly, we leverage a global self-attention module (GAM) after CNN backbone to capture wider contextual information. Secondly, due to the gradual recovery of the feature map size in the decoding stage, the local self-attention module (LAM) is employed to reduce computational complexity. With this design, the model can fuse features from global and local perspectives to better cope with scale change. Additionally, to establish the interdependence between spatial and channel dimensions, we further design a novel channel self-attention module (CAM) and combine it with LAM. Finally, we construct a simple yet useful double head module that outputs a foreground segmentation map in addition to the intermediate density map, which are then multiplied together in a pixel-wise style to suppress background interference. The experimental results on several benchmark datasets demonstrate that our method achieves remarkable improvement.