  Recently, density map regression-based methods have dominated in crowd
counting owing to their excellent fitting ability on density distribution.
However, further improvement tends to saturate mainly because of the confusing
background noise and the large density variation. In this paper, we propose a
Hierarchically Decoupled Network (HDNet) to solve the above two problems within
a unified framework. Specifically, a background classification sub-task is
decomposed from the density map prediction task, which is then assigned to a
Density Decoupling Module (DDM) to exploit its highly discriminative ability.
For the remaining foreground prediction sub-task, it is further hierarchically
decomposed to several density-specific sub-tasks by the DDM, which are then
solved by the regression-based experts in a Foreground Density Estimation
Module (FDEM). Although the proposed strategy effectively reduces the
hypothesis space so as to relieve the optimization for those task-specific
experts, the high correlation of these sub-tasks are ignored. Therefore, we
introduce three types of interaction strategies to unify the whole framework,
which are Feature Interaction, Gradient Interaction, and Scale Interaction.
Integrated with the above spirits, HDNet achieves state-of-the-art performance
on several popular counting benchmarks.
