Crowd Management in Open Spaces

TL;DR

This paper presents a robust feature-based approach for crowd management in open spaces, addressing the challenge of varying crowd densities to enhance public safety and security.

Contribution

It introduces a new method that improves crowd analysis by accounting for changing densities, evaluated on a benchmark dataset.

Findings

Effective handling of diverse crowd densities

Improved accuracy in crowd analysis tasks

Validated on a standard benchmark dataset

Abstract

Crowd analysis and management is a challenging problem to ensure public safety and security. For this purpose, many techniques have been proposed to cope with various problems. However, the generalization capabilities of these techniques is limited due to ignoring the fact that the density of crowd changes from low to extreme high depending on the scene under observation. We propose robust feature based approach to deal with the problem of crowd management for people safety and security. We have evaluated our method using a benchmark dataset and have presented details analysis.