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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.
It is observed that the switch relays an image patch to a particular CNN column based on density of crowd.
Estimating crowd count in densely crowded scenes is an extremely challenging task due to non-uniform scale variations.
#2 best model for Crowd Counting on UCF CC 50
The task of crowd counting is to automatically estimate the pedestrian number in crowd images.
In this work, we explore the cross-scale similarity in crowd counting scenario, in which the regions of different scales often exhibit high visual similarity.
Crowd counting on static images is a challenging problem due to scale variations.
Crowd counting from a single image is a challenging task due to high appearance similarity, perspective changes and severe congestion.