Paper Info
Title
A Role-dependent Data-driven Approach for High Density Crowd Behavior Modeling.
Abstract
In this paper, we propose a role-dependent data-driven modeling approach to simulate pedestrians' motion in high density scenes. It is commonly observed that pedestrians behave quite differently when walking in dense crowd. Some people explore routes towards their destinations. Meanwhile, some people deliberately follow others, leading to lane formation. Based on these observations, two roles are included in the proposed model: leader and follower. The motion behaviors of leader and follower are modeled separately. Leaders' behaviors are learned from real crowd motion data using state-action pairs while followers' behaviors are calculated based on specific targets that are obtained dynamically during the simulation. The proposed role-dependent data-driven model is trained on crowd video data in one dataset and is then applied to two other different datasets to test its generality and effectiveness. The simulation results demonstrate that the proposed role-dependent data-driven model is capable of simulating crowd behaviors in crowded scenes realistically and reproducing collective crowd behaviors such as lane formation.
Year
DOI
Venue
2016
10.1145/2901378.2901382
SIGSIM-PADS '16: SIGSIM Principles of Advanced Discrete Simulation Banff Alberta Canada May, 2016
Field
DocType
ISBN
Computer vision,Data-driven,Computer science,High density,Real-time computing,Artificial intelligence,Crowd simulation,Generality,Crowd psychology,Machine learning
Conference
978-1-4503-3742-7
Citations 
PageRank 
References 
2
0.37
12
Authors
3
Name
Order
Citations
PageRank
Mingbi Zhao1182.55
Jing-hui Zhong238033.00
Wentong Cai31928197.81