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In the last decade, crowd counting attracts much attention of researchers due to its wide-spread applications, including crowd monitoring, public safety, space design, etc.
A dense region can always be divided until sub-region counts are within the previously observed closed set.
SOTA for Crowd Counting on ShanghaiTech A
In crowd counting datasets, each person is annotated by a point, which is usually the center of the head.
Dense crowd counting aims to predict thousands of human instances from an image, by calculating integrals of a density map over image pixels.
Then to relate the density maps between neighbouring frames, a Locality-constrained Spatial Transformer (LST) module is introduced to estimate the density map of next frame with that of current frame.
This technical report attempts to provide efficient and solid kits addressed on the field of crowd counting, which is denoted as Crowd Counting Code Framework (C$^3$F).
We introduce a detection framework for dense crowd counting and eliminate the need for the prevalent density regression paradigm.
#2 best model for Crowd Counting on ShanghaiTech A
Crowd counting from a single image is a challenging task due to high appearance similarity, perspective changes and severe congestion.