Cascaded Residual Density Network for Crowd Counting

TL;DR

This paper introduces CRDNet, a novel coarse-to-fine crowd counting model that uses cascaded residual modules and local count loss to improve density map quality and counting accuracy in challenging scenes.

Contribution

The paper presents a new cascaded residual density network with a local count loss for more accurate crowd counting, addressing scale and perspective variations.

Findings

Achieves better accuracy than state-of-the-art methods on benchmark datasets.

Effectively improves density map quality through multi-scale residual modules.

Reduces counting errors with the novel local count loss.

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.