Crowd Management in Open Spaces
Tauseef Ali, Ahmed B. Altamimi

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.
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsAnomaly Detection Techniques and Applications · Video Surveillance and Tracking Methods · Fire Detection and Safety Systems
