  Crowd gatherings at social and cultural events are increasing in leaps and
bounds with the increase in population. Surveillance through computer vision
and expert decision making systems can help to understand the crowd phenomena
at large gatherings. Understanding crowd phenomena can be helpful in early
identification of unwanted incidents and their prevention. Motion flow is one
of the important crowd phenomena that can be instrumental in describing the
crowd behavior. Flows can be useful in understanding instabilities in the
crowd. However, extracting motion flows is a challenging task due to randomness
in crowd movement and limitations of the sensing device. Moreover, low-level
features such as optical flow can be misleading if the randomness is high. In
this paper, we propose a new model based on Langevin equation to analyze the
linear dominant flows in videos of densely crowded scenarios. We assume a force
model with three components, namely external force, confinement/drift force,
and disturbance force. These forces are found to be sufficient to describe the
linear or near-linear motion in dense crowd videos. The method is significantly
faster as compared to existing popular crowd segmentation methods. The
evaluation of the proposed model has been carried out on publicly available
datasets as well as using our dataset. It has been observed that the proposed
method is able to estimate and segment the linear flows in the dense crowd with
better accuracy as compared to state-of-the-art techniques with substantial
decrease in the computational overhead.
