Cascaded Residual Density Network for Crowd Counting
- URL: http://arxiv.org/abs/2107.13718v1
- Date: Thu, 29 Jul 2021 03:07:11 GMT
- Title: Cascaded Residual Density Network for Crowd Counting
- Authors: Kun Zhao, Luchuan Song, Bin Liu, Qi Chu, Nenghai Yu
- Abstract summary: We propose a novel Cascaded Residual Density Network (CRDNet) in a coarse-to-fine approach to generate the high-quality density map for crowd counting more accurately.
A novel additional local count loss is presented to refine the accuracy of crowd counting.
- Score: 63.714719914701014
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Crowd counting is a challenging task due to the issues such as scale
variation and perspective variation in real crowd scenes. In this paper, we
propose a novel Cascaded Residual Density Network (CRDNet) in a coarse-to-fine
approach to generate the high-quality density map for crowd counting more
accurately. (1) We estimate the residual density maps by multi-scale pyramidal
features through cascaded residual density modules. It can improve the quality
of density map layer by layer effectively. (2) A novel additional local count
loss is presented to refine the accuracy of crowd counting, which reduces the
errors of pixel-wise Euclidean loss by restricting the number of people in the
local crowd areas. Experiments on two public benchmark datasets show that the
proposed method achieves effective improvement compared with the
state-of-the-art methods.
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