Predicting pedestrian movement is critical for human behavior analysis and also for safe and efficient human-agent interactions. However, despite significant advancements, it is still challenging for existing approaches to capture the uncertainty and multimodality of human navigation decision making. In this paper, we propose SocialVAE, a novel approach for human trajectory prediction. The core of SocialVAE is a timewise variational autoencoder architecture that exploits stochastic recurrent neural networks to perform prediction, combined with a social attention mechanism and a backward posterior approximation to allow for better extraction of pedestrian navigation strategies. We show that SocialVAE improves current state-of-the-art performance on several pedestrian trajectory prediction benchmarks, including the ETH/UCY benchmark, Stanford Drone Dataset, and SportVU NBA movement dataset. Code is available at: https://github.com/xupei0610/SocialVAE.
@article{arxiv.2203.08207,
title = {SocialVAE: Human Trajectory Prediction using Timewise Latents},
author = {Pei Xu and Jean-Bernard Hayet and Ioannis Karamouzas},
journal= {arXiv preprint arXiv:2203.08207},
year = {2022}
}
Comments
In the 17th European Conference on Computer Vision (ECCV 2022). Code: https://github.com/xupei0610/SocialVAE