基于局部-全局双分支网络的密集人群计数方法

Convolutional neural network based crowd counting methods have promoted a significant improvement in the accuracy of crowd counting. However, for congested crowd, huge scale variations of crowd heads and complex scenes still hinder the accuracy of crowd counting. In order to overcome this problem a global-local dual branch network was proposed. The local branch was arranged with the proposed scale-aware feature extraction modules to model the scale changes of the heads in congested crowds. The global branch was arranged with a localization-aware attention module to enhance the network's ability to discriminate between the crowd and the background objects. Then the extracted local features and global features were sent to the feature fusion branch to produce a crowd density map. The proposed method was evaluated on three commonly-used crowd counting datasets and one remote sensing object counting dataset. The quantitative and qualitative results show the effectiveness of the proposed method.