GTFNet: Ground Truth Fitting Network for Crowd Counting

Abstract

Crowd counting aims to estimate the number of pedestrians in a single image. Current crowd counting methods usually obtain counting results by integrating density maps. However, the label density map generated by the Gaussian kernel cannot accurately map the ground truth in the corresponding crowd image, thereby affecting the final counting result. In this paper, a ground truth fitting network called GTFNet was proposed, which aims to generate estimated density maps which can fit the ground truth better. Firstly, the VGG network combined with the dilated convolutional layers was used as the backbone network of GTFNet to extract hierarchical features. The multi-level features were concatenated to achieve compensation for information loss caused by pooling operations, which may assist the network to obtain texture information and spatial information. Secondly, the regional consistency loss function was designed to obtain the mapping results of the estimated density map and the label density map at different region levels. During the training process, the region-level dynamic weights were designed to assign a suitable region fitting range for the network, which can effectively reduce the impact of label errors on the estimated density maps. Finally, our proposed GTFNet was evaluated upon three crowd counting datasets (ShanghaiTech, UCF_CC_50 and UCF-QRNF). The experimental results demonstrated that the proposed GTFNet achieved excellent overall performance on all these datasets.