He, F;
Yue, J;
Zhu, J;
Seyfried, A;
Casas, D;
Pettré, J;
Wang, H;
(2025)
Learning Extremely High Density Crowds as Active Matters.
In:
Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition.
(pp. pp. 540-550).
IEEE: Nashville, TN, USA.
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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 continuum 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 that can mimic 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 evaluations, 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.
Type: | Proceedings paper |
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Title: | Learning Extremely High Density Crowds as Active Matters |
Event: | 2025 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) |
Dates: | 10 Jun 2025 - 17 Jun 2025 |
Open access status: | An open access version is available from UCL Discovery |
DOI: | 10.1109/CVPR52734.2025.00059 |
Publisher version: | https://doi.org/10.1109/cvpr52734.2025.00059 |
Language: | English |
Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
URI: | https://discovery.ucl.ac.uk/id/eprint/10215207 |
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