Cascaded Residual Density Network for Crowd Counting
This addresses the problem of accurate crowd counting in real scenes with scale and perspective variations, but it appears incremental as it builds on existing methods.
The paper tackles crowd counting by proposing a Cascaded Residual Density Network (CRDNet) that uses a coarse-to-fine approach with multi-scale pyramidal features and a local count loss, achieving effective improvement on two benchmark datasets.
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.