Crowd counting aims to accurately count the number of objects in an image. Challenges from severely occlusion, large scale variation, complex background interference and non-uniform density distribution, limit the crowd number estimation accuracy. This paper proposes avel crowd counting approach based on pyramidal scale module (PSM)
Crowd counting, which towards to accurately count the number of the objects
in images, has been attracted more and more attention by researchers recently.
However, challenges from severely occlusion, large scale variation, complex
background interference and non-uniform density distribution, limit the crowd
number estimation accuracy. To mitigate above issues, this paper proposes a
novel crowd counting approach based on pyramidal scale module (PSM) and global
context module (GCM), dubbed PSCNet. Moreover, a reliable supervision manner
combined Bayesian and counting loss (BCL) is utilized to learn the density
probability and then computes the count exception at each annotation point.
Specifically, PSM is used to adaptively capture multi-scale information, which
can identify a fine boundary of crowds with different image scales. GCM is
devised with low-complexity and lightweight manner, to make the interactive
information across the channels of the feature maps more efficient, meanwhile
guide the model to select more suitable scales generated from PSM. Furthermore,
BL is leveraged to construct a credible density contribution probability
supervision manner, which relieves non-uniform density distribution in crowds
to a certain extent. Extensive experiments on four crowd counting datasets show
the effectiveness and superiority of the proposed model. Additionally, some
experiments extended on a remote sensing object counting (RSOC) dataset further
validate the generalization ability of the model. Our resource code will be
released upon the acceptance of this work.