Crowd counting is an important topic in computer vision due to its practical usage in surveillance systems. The typical design of crowd counting algorithms is divided into two steps. First, the ground-truth density maps of crowd images are generated from the ground-truth dot maps (density map generation), e.g., by convolving with a Gaussian kernel. Second, deep learning models are designed to predict a density map from an input image (density map estimation). Most research efforts have concentrated on the density map estimation problem, while the problem of density map generation has not been adequately explored. In particular, the density map could be considered as an intermediate representation used to train a crowd counting network. In the sense of end-to-end training, the hand-crafted methods used for generating the density maps may not be optimal for the particular network or dataset used. To address this issue, we first show the impact of different density maps and that better ground-truth density maps can be obtained by refining the existing ones using a learned refinement network, which is jointly trained with the counter. Then, we propose an adaptive density map generator, which takes the annotation dot map as input, and learns a density map representation for a counter. The counter and generator are trained jointly within an end-to-end framework. The experiment results on popular counting datasets confirm the effectiveness of the proposed learnable density map representations.
In: Intl. Conf. on Computer Vision (ICCV), Seoul, Oct 2019.