Learning Extremely High Density Crowds as Active Matters
This work addresses a challenging and understudied problem in computer vision for applications like crowd management and safety, though it appears incremental as it builds on existing methods with a novel physics-based twist.
The paper tackles the problem of analyzing and forecasting extremely high-density crowds in videos by proposing a new approach that models crowds as active matter, combining physics priors with neural networks. The result is a model that outperforms existing methods in these tasks, offering improved interpretability through continuous-time simulation.
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.