Congestion-aware Bayesian Loss for Crowd Counting

Abstract

Deep learning-based crowd density estimation can greatly improve the accuracy of crowd counting. Though a Bayesian loss method resolves the two problems of the need of a hand-crafted ground truth (GT) density and noisy annotations, counting accurately in high-congested scenes remains a challenging issue. In a crowd scene, people’s appearances change according to the scale of each individual (i.e., the person-scale). Also, the lower the sparsity of a local region (i.e., the crowd-sparsity), the more difficult it is to estimate the crowd density. In this paper, we propose a novel congestion-aware Bayesian loss method that considers the person-scale and crowd-sparsity. We estimate the person-scale based on scene geometry, and we then estimate the crowd-sparsity using the estimated person-scale. The estimated person-scale and crowd-sparsity are utilized in the novel congestion-aware Bayesian loss method to improve the supervising representation of the point annotations. We verified the effectiveness of each proposed component through several ablation experiments, and in the various experiments on public datasets, our proposed method achieved state-of-the-art performance. Author