  The existing crowd counting models require extensive training data, which is
time-consuming to annotate. To tackle this issue, we propose a simple yet
effective crowd counting method by utilizing the Segment-Everything-Everywhere
Model (SEEM), an adaptation of the Segmentation Anything Model (SAM), to
generate pseudo-labels for training crowd counting models. However, our initial
investigation reveals that SEEM's performance in dense crowd scenes is limited,
primarily due to the omission of many persons in high-density areas. To
overcome this limitation, we propose an adaptive resolution SEEM to handle the
scale variations, occlusions, and overlapping of people within crowd scenes.
Alongside this, we introduce a robust localization method, based on Gaussian
Mixture Models, for predicting the head positions in the predicted people
masks. Given the mask and point pseudo-labels, we propose a robust loss
function, which is designed to exclude uncertain regions based on SEEM's
predictions, thereby enhancing the training process of the counting networks.
Finally, we propose an iterative method for generating pseudo-labels. This
method aims at improving the quality of the segmentation masks by identifying
more tiny persons in high-density regions, which are often missed in the first
pseudo-labeling stage. Overall, our proposed method achieves the best
unsupervised performance in crowd counting, while also being comparable results
to some supervised methods. This makes it a highly effective and versatile tool
for crowd counting, especially in situations where labeled data is not
available.
