  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.
