The segmentation of the moving and non-moving parts in an image within the crowd analysis studies is a crucial result in terms of understanding the behaviour. However, in many studies carried out, moving areas are assessed as making a set of movement that are similar in terms of location and of acceleration. In this study, what kind of interaction there exists among the people in a high-density crowd movement was studied. Initially, the motion data is represented as trajectories by using the optical flow and particle advection methods. The image is divided into equal hexagons and each area's topological entropy value is determined through Finite Time Braid Entropy (FTBE). This value depends on the complexity of the spiral structure that the trajectories generate throughout the movement and this value shows how much effective it is in the area. Eventually, through the complexity map that was generated, the behaviour of the crowd could be better understood and assessed. Also, in this way, possible abnormal conditions in the area can be predicted by monitoring each region and by setting off from the changes in the values of Braid entropy in time. The method was tested using the high-density crowd movements in the UCF database and results were presented.