One-Shot Crowd Counting With Density Guidance For Scene Adaptaion

TL;DR

This paper introduces a novel few-shot learning approach for crowd counting that leverages local and global density features to adapt models to unseen surveillance scenes, significantly improving generalization.

Contribution

It proposes a density-guided few-shot learning framework with local density prototypes and global density features for scene adaptation in crowd counting.

Findings

Outperforms state-of-the-art methods on three surveillance datasets.

Effectively adapts to unseen scenes with limited support data.

Utilizes local density prototypes and global density features for improved accuracy.

Abstract

Crowd scenes captured by cameras at different locations vary greatly, and existing crowd models have limited generalization for unseen surveillance scenes. To improve the generalization of the model, we regard different surveillance scenes as different category scenes, and introduce few-shot learning to make the model adapt to the unseen surveillance scene that belongs to the given exemplar category scene. To this end, we propose to leverage local and global density characteristics to guide the model of crowd counting for unseen surveillance scenes. Specifically, to enable the model to adapt to the varying density variations in the target scene, we propose the multiple local density learner to learn multi prototypes which represent different density distributions in the support scene. Subsequently, these multiple local density similarity matrixes are encoded. And they are utilized to guide the model in a local way. To further adapt to the global density in the target scene, the global density features are extracted from the support image, then it is used to guide the model in a global way. Experiments on three surveillance datasets shows that proposed method can adapt to the unseen surveillance scene and outperform recent state-of-the-art methods in the few-shot crowd counting.