The recognition in real time of crowd dynamics in public places are becoming essential to avoid crowd related disasters and ensure safety of people. We present in this paper a new approach for Crowd Event Recognition. Our study begins with a novel tracking method, based on HOG descriptors, to finally use pre-defined models (i.e. crowd scenarios) to recognize crowd events. We define these scenarios using statistics analysis from the data sets used in the experimentation. The approach is characterized by combining a local analysis with a global analysis for crowd behavior recognition. The local analysis is enabled by a robust tracking method, and global analysis is done by a scenario modeling stage.