  The task of crowd counting is extremely challenging due to complicated
difficulties, especially the huge variation in vision scale. Previous works
tend to adopt a naive concatenation of multi-scale information to tackle it,
while the scale shifts between the feature maps are ignored. In this paper, we
propose a novel Hierarchical Scale Recalibration Network (HSRNet), which
addresses the above issues by modeling rich contextual dependencies and
recalibrating multiple scale-associated information. Specifically, a Scale
Focus Module (SFM) first integrates global context into local features by
modeling the semantic inter-dependencies along channel and spatial dimensions
sequentially. In order to reallocate channel-wise feature responses, a Scale
Recalibration Module (SRM) adopts a step-by-step fusion to generate final
density maps. Furthermore, we propose a novel Scale Consistency loss to
constrain that the scale-associated outputs are coherent with groundtruth of
different scales. With the proposed modules, our approach can ignore various
noises selectively and focus on appropriate crowd scales automatically.
Extensive experiments on crowd counting datasets (ShanghaiTech, MALL,
WorldEXPO'10, and UCSD) show that our HSRNet can deliver superior results over
all state-of-the-art approaches. More remarkably, we extend experiments on an
extra vehicle dataset, whose results indicate that the proposed model is
generalized to other applications.
