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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.
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).
Our results show that networks trained to regress to the ground truth targets for labeled data and to simultaneously learn to rank unlabeled data obtain significantly better, state-of-the-art results for both IQA and crowd counting.
Estimating crowd count in densely crowded scenes is an extremely challenging task due to non-uniform scale variations.
#3 best model for Crowd Counting on UCF CC 50
It is observed that the switch relays an image patch to a particular CNN column based on density of crowd.
The task of crowd counting is to automatically estimate the pedestrian number in crowd images.
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
In crowd counting datasets, each person is annotated by a point, which is usually the center of the head.
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