  The task of crowd counting is to automatically estimate the pedestrian number
in crowd images. To cope with the scale and perspective changes that commonly
exist in crowd images, state-of-the-art approaches employ multi-column CNN
architectures to regress density maps of crowd images. Multiple columns have
different receptive fields corresponding to pedestrians (heads) of different
scales. We instead propose a scale-adaptive CNN (SaCNN) architecture with a
backbone of fixed small receptive fields. We extract feature maps from multiple
layers and adapt them to have the same output size; we combine them to produce
the final density map. The number of people is computed by integrating the
density map. We also introduce a relative count loss along with the density map
loss to improve the network generalization on crowd scenes with few
pedestrians, where most representative approaches perform poorly on. We conduct
extensive experiments on the ShanghaiTech, UCF_CC_50 and WorldExpo datasets as
well as a new dataset SmartCity that we collect for crowd scenes with few
people. The results demonstrate significant improvements of SaCNN over the
state-of-the-art.
