  In this paper, we propose a novel perspective-guided convolution (PGC) for
convolutional neural network (CNN) based crowd counting (i.e. PGCNet), which
aims to overcome the dramatic intra-scene scale variations of people due to the
perspective effect. While most state-of-the-arts adopt multi-scale or
multi-column architectures to address such issue, they generally fail in
modeling continuous scale variations since only discrete representative scales
are considered. PGCNet, on the other hand, utilizes perspective information to
guide the spatially variant smoothing of feature maps before feeding them to
the successive convolutions. An effective perspective estimation branch is also
introduced to PGCNet, which can be trained in either supervised setting or
weakly-supervised setting when the branch has been pre-trained. Our PGCNet is
single-column with moderate increase in computation, and extensive experimental
results on four benchmark datasets show the improvements of our method against
the state-of-the-arts. Additionally, we also introduce Crowd Surveillance, a
large scale dataset for crowd counting that contains 13,000+ high-resolution
images with challenging scenarios.
