  Crowd counting remains a challenging task because the presence of drastic
scale variation, density inconsistency, and complex background can seriously
degrade the counting accuracy. To battle the ingrained issue of accuracy
degradation, we propose a novel and powerful network called Scale Tree Network
(STNet) for accurate crowd counting. STNet consists of two key components: a
Scale-Tree Diversity Enhancer and a Semi-supervised Multi-level Auxiliator.
Specifically, the Diversity Enhancer is designed to enrich scale diversity,
which alleviates limitations of existing methods caused by insufficient level
of scales. A novel tree structure is adopted to hierarchically parse
coarse-to-fine crowd regions. Furthermore, a simple yet effective Multi-level
Auxiliator is presented to aid in exploiting generalisable shared
characteristics at multiple levels, allowing more accurate pixel-wise
background cognition. The overall STNet is trained in an end-to-end manner,
without the needs for manually tuning loss weights between the main and the
auxiliary tasks. Extensive experiments on four challenging crowd datasets
demonstrate the superiority of the proposed method.
