  Crowd localization is to predict each instance head position in crowd
scenarios. Since the distance of instances being to the camera are variant,
there exists tremendous gaps among scales of instances within an image, which
is called the intrinsic scale shift. The core reason of intrinsic scale shift
being one of the most essential issues in crowd localization is that it is
ubiquitous in crowd scenes and makes scale distribution chaotic.
  To this end, the paper concentrates on access to tackle the chaos of the
scale distribution incurred by intrinsic scale shift. We propose Gaussian
Mixture Scope (GMS) to regularize the chaotic scale distribution. Concretely,
the GMS utilizes a Gaussian mixture distribution to adapt to scale distribution
and decouples the mixture model into sub-normal distributions to regularize the
chaos within the sub-distributions. Then, an alignment is introduced to
regularize the chaos among sub-distributions. However, despite that GMS is
effective in regularizing the data distribution, it amounts to dislodging the
hard samples in training set, which incurs overfitting. We assert that it is
blamed on the block of transferring the latent knowledge exploited by GMS from
data to model. Therefore, a Scoped Teacher playing a role of bridge in
knowledge transform is proposed. What' s more, the consistency regularization
is also introduced to implement knowledge transform. To that effect, the
further constraints are deployed on Scoped Teacher to derive feature
consistence between teacher and student end.
  With proposed GMS and Scoped Teacher implemented on five mainstream datasets
of crowd localization, the extensive experiments demonstrate the superiority of
our work. Moreover, comparing with existing crowd locators, our work achieves
state-of-the-art via F1-meansure comprehensively on five datasets.
