  Crowd counting is an important vision task, which faces challenges on
continuous scale variation within a given scene and huge density shift both
within and across images. These challenges are typically addressed using
multi-column structures in existing methods. However, such an approach does not
provide consistent improvement and transferability due to limited ability in
capturing multi-scale features, sensitiveness to large density shift, and
difficulty in training multi-branch models. To overcome these limitations, a
Single-column Scale-invariant Network (ScSiNet) is presented in this paper,
which extracts sophisticated scale-invariant features via the combination of
interlayer multi-scale integration and a novel intralayer scale-invariant
transformation (SiT). Furthermore, in order to enlarge the diversity of
densities, a randomly integrated loss is presented for training our
single-branch method. Extensive experiments on public datasets demonstrate that
the proposed method consistently outperforms state-of-the-art approaches in
counting accuracy and achieves remarkable transferability and scale-invariant
property.
