SocialVAE: Human Trajectory Prediction using Timewise Latents

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 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, the 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},
    doi={10.1007/978-3-031-19772-7_30}
}