Publication
Human motion trajectory prediction using the Social Force Model for real-time and low computational cost applications
Conference Article
Conference
Iberian Robotics Conference (ROBOT)
Edition
6th
Pages
1-12
Doc link
https://drive.google.com/file/d/1WT983sRsZXZgL5zOSILUC3RvAA2qsyhh/view
File
Abstract
Human motion trajectory prediction is a very important functionality for human-robot collaboration, specifically in accompany- ing, guiding, or approaching tasks, but also in social robotics, self-driving vehicles, or security systems. In this paper, a novel trajectory prediction model, Social Force Generative Adversarial Network (SoFGAN), is pro- posed. SoFGAN uses a Generative Adversarial Network (GAN) and So- cial Force Model (SFM) to generate different plausible people trajectories reducing collisions in a scene. Furthermore, a Conditional Variational Au- toencoder (CVAE) module is added to emphasize the destination learn- ing. We show that our method is more accurate in making predictions in UCY or BIWI datasets than most of the current state-of-the-art models and also reduces collisions in comparison to other approaches. Through real-life experiments, we demonstrate that the model can be used in real-time without GPU’s to perform good quality predictions with a low computational cost.
Categories
automation.
Author keywords
Human Motion Prediction, Social Force Model, Generative Adversarial Network, Conditional Variational Autoencoder
Scientific reference
O. Gil and A. Sanfeliu. Human motion trajectory prediction using the Social Force Model for real-time and low computational cost applications, 6th Iberian Robotics Conference, 2023, Portugal, pp. 1-12, Springer, to appear.
Follow us!