Measuring Crowd Collectiveness by Macroscopic and Microscopic Motion Consistencies

Yi, Zou; Xu, Zeshui; Liu, Yuncai

doi:10.1109/tmm.2018.2832601

Cited by 5 publications

(1 citation statement)

References 34 publications

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“…In recent years, researchers analyzed crowd motions with computer vision technology to extract real-time information for city emergency managers [3][4][5]. For instance, crowd collectiveness was proposed as a quantitative indicator to measure the degree of orderliness of crowd motions [6,7]. From another perspective, abnormal behaviors were detected from crowd motions to reveal the potential hazards for social security [8,9].…”

Section: Introductionmentioning

confidence: 99%

Modeling Pedestrian Motion in Crowded Scenes Based on the Shortest Path Principle

Zou

Liu

2021

Applied Sciences

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In the computer vision field, understanding human dynamics is not only a great challenge but also very meaningful work, which plays an indispensable role in public safety. Despite the complexity of human dynamics, physicists have found that pedestrian motion in a crowd is governed by some internal rules, which can be formulated as a motion model, and an effective model is of great importance for understanding and reconstructing human dynamics in various scenes. In this paper, we revisit the related research in social psychology and propose a two-part motion model based on the shortest path principle. One part of the model seeks the origin and destination of a pedestrian, and the other part generates the movement path of the pedestrian. With the proposed motion model, we simulated the movement behavior of pedestrians and classified them into various patterns. We next reconstructed the crowd motions in a real-world scene. In addition, to evaluate the effectiveness of the model in crowd motion simulations, we created a new indicator to quantitatively measure the correlation between two groups of crowd motion trajectories. The experimental results show that our motion model outperformed the state-of-the-art model in the above applications.

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