  Traditional crowd counting approaches usually use Gaussian assumption to
generate pseudo density ground truth, which suffers from problems like
inaccurate estimation of the Gaussian kernel sizes. In this paper, we propose a
new measure-based counting approach to regress the predicted density maps to
the scattered point-annotated ground truth directly. First, crowd counting is
formulated as a measure matching problem. Second, we derive a semi-balanced
form of Sinkhorn divergence, based on which a Sinkhorn counting loss is
designed for measure matching. Third, we propose a self-supervised mechanism by
devising a Sinkhorn scale consistency loss to resist scale changes. Finally, an
efficient optimization method is provided to minimize the overall loss
function. Extensive experiments on four challenging crowd counting datasets
namely ShanghaiTech, UCF-QNRF, JHU++, and NWPU have validated the proposed
method.
