SocialVAE: Human Trajectory Prediction
using Timewise Latents

Pei Xu^1,3, Jean-Bernard Hayet², Ioannis Karamouzas¹

¹Clemson University, ²CIMAT, A.C., Mexico, ³Roblox

In the 17th European Conference on Computer Vision (ECCV 2022)

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.

@inproceedings{socialvae2022,
 author = {Xu, Pei and Hayet, Jean-Bernard and Karamouzas, Ioannis},
 title = {SocialVAE: Human Trajectory Prediction using Timewise Latents},
 booktitle = {European Conference on Computer Vision},
 pages = {511-528},
 year = {2022},
 organization={Springer}
}