Search Results for author: Seonghyeon Moon

Found 4 papers, 1 papers with code

Laying the Foundations of Deep Long-Term Crowd Flow Prediction

1 code implementation ECCV 2020 Samuel S. Sohn, Honglu Zhou, Seonghyeon Moon, Sejong Yoon, Vladimir Pavlovic, Mubbasir Kapadia

Predicting the crowd behavior in complex environments is a key requirement for crowd and disaster management, architectural design, and urban planning.

MUSE-VAE: Multi-Scale VAE for Environment-Aware Long Term Trajectory Prediction

no code implementations18 Jan 2022 Mihee Lee, Samuel S. Sohn, Seonghyeon Moon, Sejong Yoon, Mubbasir Kapadia, Vladimir Pavlovic

Accurate long-term trajectory prediction in complex scenes, where multiple agents (e. g., pedestrians or vehicles) interact with each other and the environment while attempting to accomplish diverse and often unknown goals, is a challenging stochastic forecasting problem.

Trajectory Prediction

Deep Crowd-Flow Prediction in Built Environments

no code implementations13 Oct 2019 Samuel S. Sohn, Seonghyeon Moon, Honglu Zhou, Sejong Yoon, Vladimir Pavlovic, Mubbasir Kapadia

In this paper, we propose an approach to instantly predict the long-term flow of crowds in arbitrarily large, realistic environments.

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