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arXiv · 2503.12168

Learning Extremely High Density Crowds as Active Matters

Feixiang He·Jiangbei Yue·Jialin Zhu·Armin Seyfried·Dan Casas·Julien Pettré·He Wang
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Abstract

Video-based high-density crowd analysis and prediction has been a long-standing topic in computer vision. It is notoriously difficult due to, but not limited to, the lack of high-quality data and complex crowd dynamics. Consequently, it has been relatively under studied. In this paper, we propose a new approach that aims to learn from in-the-wild videos, often with low quality where it is difficult to track individuals or count heads. The key novelty is a new physics prior to model crowd dynamics. We model high-density crowds as active matter, a continumm with active particles subject to stochastic forces, named 'crowd material'. Our physics model is combined with neural networks, resulting in a neural stochastic differential equation system which can mimic the complex crowd dynamics. Due to the lack of similar research, we adapt a range of existing methods which are close to ours for comparison. Through exhaustive evaluation, we show our model outperforms existing methods in analyzing and forecasting extremely high-density crowds. Furthermore, since our model is a continuous-time physics model, it can be used for simulation and analysis, providing strong interpretability. This is categorically different from most deep learning methods, which are discrete-time models and black-boxes.

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Feixiang He, Jiangbei Yue, Jialin Zhu, Armin Seyfried, Dan Casas, Julien Pettré, He Wang. 2025-03-15. Learning Extremely High Density Crowds as Active Matters. https://arxiv.org/abs/2503.12168

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