Crowd Synthesis Based on Hybrid Simulation Rules for Complex Behaviour Analysis

Yu Hao, Zhijie Xu, Ying Liu, Jing Wang, Jiulun Fan

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The acquirement of video data for crowd anomaly detection and behavior analysis is a challenging practical issue due to the short of and deficiencies within real-life video footages. The construction of simulated crowd scenes using real actors is costly and often carrying potential safety hazards. In order to address this issue, a simulation or synthesis-based crowd video generation techniques is proposed and explored in this research through the investigation and integration of various behavioral models from physics, social science and human psychology fields. The investigation has been focusing on the separation and convergence behaviors among agents from different groups within a large crowd. This research also introduced an innovative crowd grouping model through adopting the concept of Velocity Perception based Social Force Model (VPSFM) and the Boids behavioral model. In the experiments the devised model successfully empowered a game-engine-driven crowd scene simulator that is capable of configuring and generating random crowd scenes of desired aesthesis, visual and behavioral realism. Furthermore, based on the proposed model, a defined grouping attraction force is proven effective when utilized to segment the randomly distributed and mixed crowds.

Original languageEnglish
Title of host publication2018 24th IEEE International Conference on Automation and Computing (ICAC 2018)
Subtitle of host publicationImproving Productivity through Automation and Computing
EditorsXiandong Ma
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages6
ISBN (Electronic)9781862203419
ISBN (Print)9781538648919
DOIs
Publication statusPublished - 1 Jul 2019
Event24th IEEE International Conference on Automation and Computing: Improving Productivity through Automation and Computing - Newcastle University, Newcastle upon Tyne, United Kingdom
Duration: 6 Sep 20187 Sep 2018
Conference number: 24
https://ieeexplore.ieee.org/xpl/conhome/8742895/proceeding (Website Containing the Proceedings)
http://www.cacsuk.co.uk/index.php/conferences/icac (Link to Conference Information)

Conference

Conference24th IEEE International Conference on Automation and Computing
Abbreviated titleICAC 2018
CountryUnited Kingdom
CityNewcastle upon Tyne
Period6/09/187/09/18
Internet address

Fingerprint

Hybrid Simulation
Synthesis
Grouping
Model
Social sciences
Anomaly Detection
Social Sciences
Hazard
Hazards
Simulator
Engine
Physics
Simulators
Safety
Game
Engines
Experiment
Simulation

Cite this

Hao, Y., Xu, Z., Liu, Y., Wang, J., & Fan, J. (2019). Crowd Synthesis Based on Hybrid Simulation Rules for Complex Behaviour Analysis. In X. Ma (Ed.), 2018 24th IEEE International Conference on Automation and Computing (ICAC 2018): Improving Productivity through Automation and Computing [8749070] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.23919/IConAC.2018.8749070
Hao, Yu ; Xu, Zhijie ; Liu, Ying ; Wang, Jing ; Fan, Jiulun. / Crowd Synthesis Based on Hybrid Simulation Rules for Complex Behaviour Analysis. 2018 24th IEEE International Conference on Automation and Computing (ICAC 2018): Improving Productivity through Automation and Computing. editor / Xiandong Ma. Institute of Electrical and Electronics Engineers Inc., 2019.
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title = "Crowd Synthesis Based on Hybrid Simulation Rules for Complex Behaviour Analysis",
abstract = "The acquirement of video data for crowd anomaly detection and behavior analysis is a challenging practical issue due to the short of and deficiencies within real-life video footages. The construction of simulated crowd scenes using real actors is costly and often carrying potential safety hazards. In order to address this issue, a simulation or synthesis-based crowd video generation techniques is proposed and explored in this research through the investigation and integration of various behavioral models from physics, social science and human psychology fields. The investigation has been focusing on the separation and convergence behaviors among agents from different groups within a large crowd. This research also introduced an innovative crowd grouping model through adopting the concept of Velocity Perception based Social Force Model (VPSFM) and the Boids behavioral model. In the experiments the devised model successfully empowered a game-engine-driven crowd scene simulator that is capable of configuring and generating random crowd scenes of desired aesthesis, visual and behavioral realism. Furthermore, based on the proposed model, a defined grouping attraction force is proven effective when utilized to segment the randomly distributed and mixed crowds.",
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Hao, Y, Xu, Z, Liu, Y, Wang, J & Fan, J 2019, Crowd Synthesis Based on Hybrid Simulation Rules for Complex Behaviour Analysis. in X Ma (ed.), 2018 24th IEEE International Conference on Automation and Computing (ICAC 2018): Improving Productivity through Automation and Computing., 8749070, Institute of Electrical and Electronics Engineers Inc., 24th IEEE International Conference on Automation and Computing, Newcastle upon Tyne, United Kingdom, 6/09/18. https://doi.org/10.23919/IConAC.2018.8749070

Crowd Synthesis Based on Hybrid Simulation Rules for Complex Behaviour Analysis. / Hao, Yu; Xu, Zhijie; Liu, Ying; Wang, Jing; Fan, Jiulun.

2018 24th IEEE International Conference on Automation and Computing (ICAC 2018): Improving Productivity through Automation and Computing. ed. / Xiandong Ma. Institute of Electrical and Electronics Engineers Inc., 2019. 8749070.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Hao Y, Xu Z, Liu Y, Wang J, Fan J. Crowd Synthesis Based on Hybrid Simulation Rules for Complex Behaviour Analysis. In Ma X, editor, 2018 24th IEEE International Conference on Automation and Computing (ICAC 2018): Improving Productivity through Automation and Computing. Institute of Electrical and Electronics Engineers Inc. 2019. 8749070 https://doi.org/10.23919/IConAC.2018.8749070