We propose in this paper an approach for recognizing group of people behaviors using multiple cameras with overlapping FOVs (Field Of View). In this context, Behavior recognition first relies on low level motion detection and frame to frame tracking which generate a graph of mobile objects for each camera. Second, to take advantage of all cameras observing the same scene, a combination mechanism is performed to combine the graphs computed for each camera into a global one. This global graph is then used for long term tracking of groups of people evolving in the scene. Finally, the result of the group tracking is used by a higher level module which recognizes predefined scenarios corresponding to specific group behaviors. This article focuses on the graphs combination mechanism and on the recognition of group behaviors. At the end, results on these two algorithms are described.