  Concerts, protests, and sporting events are occurring with increasing
frequency and magnitude. The extreme physical conditions common to these events
are known to cause injuries and loss-of-life due to the emergence of collective
motion such as crowd crush, turbulence, and density waves. Mathematical models
of human crowds aimed at enhancing crowd safety by understanding these
phenomena are developed with input from a variety of disciplines. However,
model validation is challenged by a lack of high-quality empirical data and
ethical constraints surrounding human crowd research. Consequently, generalized
model-based approach for real-time monitoring/risk-assessment of crowd
collective motion remains an open problem. Here, we take a model-free approach
to crowd analysis and show that emergent collective motion can be forecasted
directly from video data. We use mode analysis methods from material science
and concepts from non-equilibrium physics to study footage of a human crowd at
an Oasis rock concert. We analyze the attendees positional fluctuations during
a period of crowd turbulence to predict the spatial patterns of an emergent
human density wave. In addition to predicting spatial patterns of collective
motion, we also identify and measure temporal patterns that precede the density
wave and forecast its appearance by 1~s. Looking ahead, widening this
forecasting window beyond 1~s will enable new computer vision technologies for
real-time risk-assessment of emergent human collective motion.
