Crowd Counting and Individual Localization Using Pseudo Square Label

Recent work in crowd counting focuses on counting over detected individuals rather than estimating the number of people in the image. However, existing crowd localization methods directly detect the head point or region of individuals, which may entail non-responsibility of the outputs that fall outside the grid. Our proposed Pseudo Square Label Network (PSL-Net) presents a novel method for crowd counting and localization, which takes advantage of the anchor-free detection in which PSL-Net predicts the probability of the center point that fall into the responsible grid, while indirectly detecting an individual outside of the responsible grid through box regression and centerness estimation. This study proposes to supervise with pseudo square label(PSL), which is generated around point annotation with fixed size. Furthermore, we design a partial many-to-one matching algorithm to assign precise labels by only matching within PSL during the training phase, and associate the predicted points with their responsible grids through centerness during the inference phase. As a result, not only PSL-Net achieves state-of-the-art on ShanghaiTech Part A and B, which are the most popular datasets in crowd counting, but also achieves state-of-the-art among the point detection-based methods in crowd localization.