Detecting and Classifying Dominant Crowd Movements through Particle Advection

Abstract

Today, trying to understand what kind of behaviour the crowd shows by studying the data from surveillance systems is an important topic for researchers of computer vision. The aim of this study make the motion data that is at pixel level and that is obtained by optical flow method a more meaningful data set with the particle advection method. In other words, the aim is to monitor the motion data by converting the 3D optical flow data set to a 2D data set. With this respect, evaluating how the crowd motion changes throughout the video by focusing only on the moments where there is motion can be carried out easily. At the end of the study, the success of the method is presented by testing how the image acts as a dominant movement.