Due to recent advances in new camera technologies and the Internet, millions of videos can be easily accessed from any place at any time. A significant amount of these videos are for surveillance, and include actors such as humans and vehicles performing different actions in dynamic scenes. The goal of this study is to analyze human crowd motions in videos. More specifically, moving humans are tracked throughout a video sequence, and the collective crowd motions are then clustered using path similarities via the Dominant Sets method. Moreover, this clustering result can be used to predict the coherency of the motion as a scalar value.