# Estimation of Linear Motion in Dense Crowd Videos using Langevin Model

## TL;DR

This paper introduces a Langevin model-based approach to estimate and segment linear crowd flows in dense videos, improving accuracy and speed over existing methods by modeling crowd motion with external, confinement, and disturbance forces.

## Contribution

The paper presents a novel Langevin equation-based model for analyzing linear crowd flows, offering faster computation and better accuracy than current crowd segmentation techniques.

## Key findings

- Outperforms state-of-the-art crowd segmentation methods.
- Reduces computational overhead significantly.
- Accurately estimates linear crowd flows in dense scenarios.

## Abstract

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.