  In this paper, we propose a novel self-training approach named Crowd-SDNet
that enables a typical object detector trained only with point-level
annotations (i.e., objects are labeled with points) to estimate both the center
points and sizes of crowded objects. Specifically, during training, we utilize
the available point annotations to supervise the estimation of the center
points of objects directly. Based on a locally-uniform distribution assumption,
we initialize pseudo object sizes from the point-level supervisory information,
which are then leveraged to guide the regression of object sizes via a
crowdedness-aware loss. Meanwhile, we propose a confidence and order-aware
refinement scheme to continuously refine the initial pseudo object sizes such
that the ability of the detector is increasingly boosted to detect and count
objects in crowds simultaneously. Moreover, to address extremely crowded
scenes, we propose an effective decoding method to improve the detector's
representation ability. Experimental results on the WiderFace benchmark show
that our approach significantly outperforms state-of-the-art point-supervised
methods under both detection and counting tasks, i.e., our method improves the
average precision by more than 10% and reduces the counting error by 31.2%.
Besides, our method obtains the best results on the crowd counting and
localization datasets (i.e., ShanghaiTech and NWPU-Crowd) and vehicle counting
datasets (i.e., CARPK and PUCPR+) compared with state-of-the-art
counting-by-detection methods. The code will be publicly available at
https://github.com/WangyiNTU/Point-supervised-crowd-detection.
