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Creators/Authors contains: "Hayet, Jean-Bernard"

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  1. ; ; (, IEEE Robotics and Automation Letters)
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  2. ; ; (, European Conference on Computer Vision)
    Avidan, S.; Brostow, G.; Cissé, M.; Farinella, G.M.; Hassner, T. (Ed.)
    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. 
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