Crowd Management in Open Spaces
This work addresses crowd management for public safety, but it appears incremental as it builds on existing techniques with a focus on handling density variations.
The paper tackles the problem of crowd management for public safety by addressing the limited generalization of existing techniques due to varying crowd densities, proposing a robust feature-based approach and evaluating it on a benchmark dataset.
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.