A learning based approach for social force model parameter estimation

Zhiqiang Wan, Xuemin Hu, Haibo He, Yi Guo

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

11 Scopus citations

Abstract

Understanding human behavior is crucial for planning evacuation strategies when an emergency occurs. The social force model, which is a successful quantitative model, has been widely used in investigating human behavior. In this paper, we propose a gradient descent based parameter optimization method to learn the parameters of the social force model from experimental data. Although the original social force model has achieved great success, it does not consider the fact that the response of humans to that happening in front of them is stronger than that happening behind them. In order to model the directional dependency of the interactive force, we propose a modified social force model. Experimental results demonstrate the effectiveness of the modified model.

Original languageEnglish
Title of host publication2017 International Joint Conference on Neural Networks, IJCNN 2017 - Proceedings
Pages4058-4064
Number of pages7
ISBN (Electronic)9781509061815
DOIs
StatePublished - 30 Jun 2017
Event2017 International Joint Conference on Neural Networks, IJCNN 2017 - Anchorage, United States
Duration: 14 May 201719 May 2017

Publication series

NameProceedings of the International Joint Conference on Neural Networks
Volume2017-May

Conference

Conference2017 International Joint Conference on Neural Networks, IJCNN 2017
Country/TerritoryUnited States
CityAnchorage
Period14/05/1719/05/17

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