A learning based approach for social force model parameter estimation
Date of Original Version
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.
Proceedings of the International Joint Conference on Neural Networks
Wan, Zhiqiang, Xuemin Hu, Haibo He, and Yi Guo. "A learning based approach for social force model parameter estimation." Proceedings of the International Joint Conference on Neural Networks 2017-May, (2017): 4058-4064. doi:10.1109/IJCNN.2017.7966368.