Detecting interaction/complexity within crowd movements using braid entropy

Akpulat M., EKİNCİ M.

FRONTIERS OF INFORMATION TECHNOLOGY & ELECTRONIC ENGINEERING, vol.20, no.6, pp.849-861, 2019 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 20 Issue: 6
  • Publication Date: 2019
  • Doi Number: 10.1631/fitee.1800313
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.849-861
  • Keywords: Crowd behavior, Motion segmentation, Motion entropy, Crowd scene analysis, Complexity detection, Braid entropy, FLOW, MOTION, MODEL, SIMULATION
  • Erciyes University Affiliated: No


The segmentation of moving and non-moving regions in an image within the field of crowd analysis is a crucial process in terms of understanding crowd behavior. In many studies, similar movements were segmented according to the location, adjacency to each other, direction, and average speed. However, these segments may not in turn indicate the same types of behavior in each region. The purpose of this study is to better understand crowd behavior by locally measuring the degree of interaction/complexity within the segment. For this purpose, the flow of motion in the image is primarily represented as a series of trajectories. The image is divided into hexagonal cells and the finite time braid entropy (FTBE) values are calculated according to the different projection angles of each cell. These values depend on the complexity of the spiral structure that the trajectories generated throughout the movement and show the degree of interaction among pedestrians. In this study, behaviors of different complexities determined in segments are pictured as similar movements on the whole. This study has been tested on 49 different video sequences from the UCF and CUHK databases.