DiffusionLoc: A diffusion model-based framework for crowd localization

Abstract

The accurate location of individuals in dense crowds remains a challenging problem and is of significant importance for crowd analysis. Traditional methods, such as box-based and map-based approaches, often fail to achieve ideal accuracy in high-density scenarios. Point-based localization methods have recently shown promising results but generally rely on heuristic priors to address localization tasks. This reliance on priors can lead to unstable performance across diverse scenarios, especially in crowds with significant density variations, where the methods struggle to generalize effectively. In this work, we introduce a framework called DiffusionLoc built upon the diffusion models, which directly generates target points from random noise, simplifying the pipeline of point-based methods. Moreover, we design a feature interpolation method, called Differential Attention-based Implicit Feature Interpolation (DF-IFI), which effectively mitigates the instability of noisy points while extracting their features. Extensive experiments show that DiffusionLoc demonstrates superior competitive performance, and adapts flexibly to different scenarios by dynamically modifying the number of noisy points and iteration steps.