Kernel-Based Density Map Generation for Dense Object Counting

Crowd counting is an essential 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). The density map based counting methods that incorporate density map as the intermediate representation have improved counting performance dramatically. However, 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 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. We also show that the proposed framework can be applied to general dense object counting tasks. Extensive experiments are conducted on 10 datasets for 3 applications: crowd counting, vehicle counting, and general object counting. The experiment results on these datasets confirm the effectiveness of the proposed learnable density map representations.

Automated summary

Crowd counting is an important topic in computer vision, which uses density maps generated from ground-truth dot maps and deep learning models to improve counting performance. However, the hand-crafted methods used for generating density maps may not be optimal for specific networks or datasets. To address this, an adaptive density map generator is proposed, which is trained jointly with a counter within an end-to-end framework.