Local Information Matters: A Rethink of Crowd Counting

Tianhang Pan; Xiuyi Jia

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arXiv · 2508.16970

Local Information Matters: A Rethink of Crowd Counting

Tianhang Pan·Xiuyi Jia

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Abstract

The motivation of this paper originates from rethinking an essential characteristic of crowd counting: individuals (heads of humans) in the crowd counting task typically occupy a very small portion of the image. This characteristic has never been the focus of existing works: they typically use the same backbone as other visual tasks and pursue a large receptive field. This drives us to propose a new model design principle of crowd counting: emphasizing local modeling capability of the model. We follow the principle and design a crowd counting model named Local Information Matters Model (LIMM). The main innovation lies in two strategies: a window partitioning design that applies grid windows to the model input, and a window-wise contrastive learning design to enhance the model's ability to distinguish between local density levels. Moreover, a global attention module is applied to the end of the model to handle the occasionally occurring large-sized individuals. Extensive experiments on multiple public datasets illustrate that the proposed model shows a significant improvement in local modeling capability (8.7\% in MAE on the JHU-Crowd++ high-density subset for example), without compromising its ability to count large-sized ones, which achieves state-of-the-art performance. Code is available at: https://github.com/tianhangpan/LIMM.

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Tianhang Pan, Xiuyi Jia. 2025-08-23. Local Information Matters: A Rethink of Crowd Counting. https://arxiv.org/abs/2508.16970

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