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• W4313196348 abstract "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." @default.
• W4313196348 created "2023-01-06" @default.
• W4313196348 creator A5031182577 @default.
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• W4313196348 date "2022-01-01" @default.
• W4313196348 modified "2023-10-01" @default.
• W4313196348 title "SocialVAE: Human Trajectory Prediction Using Timewise Latents" @default.
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• W4313196348 doi "https://doi.org/10.1007/978-3-031-19772-7_30" @default.
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