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Crowd counting remains a challenging task because the presence of drasticscale variation, density inconsistency, and complex background can seriouslydegrade the counting accuracy. To battle the ingrained issue of accuracydegradation, we propose a novel and powerful network called Scale Tree Network(STNet) for accurate crowd counting. STNet consists of two key components: aScale-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 levelof scales. A novel tree structure is adopted to hierarchically parsecoarse-to-fine crowd regions. Furthermore, a simple yet effective Multi-levelAuxiliator is presented to aid in exploiting generalisable sharedcharacteristics at multiple levels, allowing more accurate pixel-wisebackground cognition. The overall STNet is trained in an end-to-end manner,without the needs for manually tuning loss weights between the main and theauxiliary tasks. Extensive experiments on four challenging crowd datasetsdemonstrate the superiority of the proposed method.

STNet: Scale Tree Network with Multi-level Auxiliator for Crowd Counting