TY - GEN
T1 - A Graphical simulator for modeling complex crowd behaviors
AU - Hao, Yu
AU - Xu, Zhijie
AU - Liu, Ying
AU - Wang, Jing
AU - Fan, Jiulun
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/12/5
Y1 - 2018/12/5
N2 - Abnormal crowd behaviors of varied real-world settings could represent or pose serious threat to public safety. The video data required for relevant analysis are often difficult to acquire due to security, privacy and data protection issues. Without large amounts of realistic crowd data, it is difficult to develop and verify crowd behavioral models, event detection techniques, and corresponding test and evaluations. This paper presented a synthetic method for generating crowd movements and tendency based on existing social and behavioral studies. Graph and tree searching algorithms as well as game engine-enabled techniques have been adopted in the study. The main outcomes of this research include a categorization model for entity-based behaviors following a linear aggregation approach; and the construction of an innovative agent-based pipeline for the synthesis of A-Star path-finding algorithm and an enhanced Social Force Model. A Spatial-Temporal Texture (STT) technique has been adopted for the evaluation of the model's effectiveness. Tests have highlighted the visual similarities between STTs extracted from the simulations and their counterparts-video recordings-from the real-world.
AB - Abnormal crowd behaviors of varied real-world settings could represent or pose serious threat to public safety. The video data required for relevant analysis are often difficult to acquire due to security, privacy and data protection issues. Without large amounts of realistic crowd data, it is difficult to develop and verify crowd behavioral models, event detection techniques, and corresponding test and evaluations. This paper presented a synthetic method for generating crowd movements and tendency based on existing social and behavioral studies. Graph and tree searching algorithms as well as game engine-enabled techniques have been adopted in the study. The main outcomes of this research include a categorization model for entity-based behaviors following a linear aggregation approach; and the construction of an innovative agent-based pipeline for the synthesis of A-Star path-finding algorithm and an enhanced Social Force Model. A Spatial-Temporal Texture (STT) technique has been adopted for the evaluation of the model's effectiveness. Tests have highlighted the visual similarities between STTs extracted from the simulations and their counterparts-video recordings-from the real-world.
KW - Agent-Modeling
KW - Crowd-Behavior-Simulation
KW - Social-Force-Models
KW - Spatial-Temporal-Texture
UR - http://www.scopus.com/inward/record.url?scp=85060160364&partnerID=8YFLogxK
U2 - 10.1109/iV.2018.00012
DO - 10.1109/iV.2018.00012
M3 - Conference Proceeding
AN - SCOPUS:85060160364
T3 - Information Visualisation - Biomedical Visualization, Visualisation on Built and Rural Environments and Geometric Modelling and Imaging, IV 2018
SP - 12
EP - 18
BT - Information Visualisation - Biomedical Visualization, Visualisation on Built and Rural Environments and Geometric Modelling and Imaging, IV 2018
A2 - Pires, Joao Moura
A2 - Datia, Nuno Miguel Soares
A2 - Polese, Giuseppe
A2 - Temperini, Marco
A2 - Sciarrone, Filippo
A2 - Risi, Michele
A2 - Venturini, Gilles
A2 - Di Mascio, Tania
A2 - Zaccagnino, Rocco
A2 - Deufemia, Vincenzo
A2 - Malandrino, Delfina
A2 - Diaz, Paloma
A2 - Anta, Antonio Fernandez
A2 - Banissi, Ebad
A2 - Wyeld, Theodor G.
A2 - Sarfraz, Muhammad
A2 - Bouali, Fatma
A2 - Bannatyne, Mark W. McK.
A2 - Papadopoulo, Fragkiskos
A2 - Erra, Ugo
A2 - Rossano, Veronica
A2 - Ursyn, Anna
A2 - Cuzzocrea, Alfredo
A2 - Francese, Rita
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 22nd International Conference Information Visualisation - Biomedical Visualization, Visualisation on Built and Rural Environments and Geometric Modelling and Imaging, IV 2018
Y2 - 10 July 2018 through 13 July 2018
ER -