Progressive Multi-resolution Loss for Crowd Counting
This work addresses crowd counting for applications like surveillance and event management, offering an incremental improvement over existing loss functions.
The paper tackles the problem of crowd counting by proposing a multi-resolution loss function that improves density map prediction, achieving state-of-the-art performance on datasets like ShanghaiTech A & B, UCF-QNRF, and JHU-Crowd++.
Crowd counting is usually handled in a density map regression fashion, which is supervised via a L2 loss between the predicted density map and ground truth. To effectively regulate models, various improved L2 loss functions have been proposed to find a better correspondence between predicted density and annotation positions. In this paper, we propose to predict the density map at one resolution but measure the density map at multiple resolutions. By maximizing the posterior probability in such a setting, we obtain a log-formed multi-resolution L2-difference loss, where the traditional single-resolution L2 loss is its particular case. We mathematically prove it is superior to a single-resolution L2 loss. Without bells and whistles, the proposed loss substantially improves several baselines and performs favorably compared to state-of-the-art methods on four crowd counting datasets, ShanghaiTech A & B, UCF-QNRF, and JHU-Crowd++.