We address the problem of crowd localization, i.e., the prediction of dots corresponding to people in a crowded scene. Due to various challenges, a localization method is prone to spatial semantic errors, i.e., predicting multiple dots within a same person or collapsing multiple dots in a cluttered region. We propose a topological approach targeting these semantic errors. We introduce a topological constraint that teaches the model to reason about the spatial arrangement of dots. To enforce this constraint, we define a persistence loss based on the theory of persistent homology. The loss compares the topographic landscape of the likelihood map and the topology of the ground truth. Topological reasoning improves the quality of the localization algorithm especially near cluttered regions. On multiple public benchmarks, our method outperforms previous localization methods. Additionally, we demonstrate the potential of our method in improving the performance in the crowd counting task.

本文提出一种基于拓扑学方法的众包定位算法，引入一种基于持久同调理论的持久损失函数，通过对空间点的拓扑约束来避免语义错误，提高了在拥挤区域中的定位精度，并在公共基准测试中表现优越，同时还对人群分布的研究有着潜在的拓展应用。

拓扑约束下的众包本地化