  Crowd localization aims to predict the spatial position of humans in a crowd
scenario. We observe that the performance of existing methods is challenged
from two aspects: (i) ranking inconsistency between test and training phases;
and (ii) fixed anchor resolution may underfit or overfit crowd densities of
local regions. To address these problems, we design a supervision target
reassignment strategy for training to reduce ranking inconsistency and propose
an anchor pyramid scheme to adaptively determine the anchor density in each
image region. Extensive experimental results on three widely adopted datasets
(ShanghaiTech A\&B, JHU-CROWD++, UCF-QNRF) demonstrate the favorable
performance against several state-of-the-art methods.
