CPSPNet: Crowd Counting via Semantic Segmentation Framework

Abstract

Crowd counting, i.e., estimation number of the pedestrian in crowd images, is emerging as an essential research problem with the public security applications. The density-based method of crowd counting still has some challenges, such as lack of perspective information in density map and background noise. Current models often misjudge background noise as a person and the ground truth density map widely used now is not so accurate. In this paper, we present a novel approach to help generate a higher quality density map. On the one hand, we eliminate the apparent mistakes in the density map with the help of a semantic segmentation model, which provides more information about fine-granted negative samples. On the other hand, we modify the density map to make sure it maintains a natural attribute. The experimental results prove the effectiveness of our method for crowd counting models, especially in uneven distribution monitoring scenario.