Regularising neural networks for future trajectory prediction via inverse reinforcement learning framework

Regularising neural networks for future trajectory prediction via inverse reinforcement learning framework

Predicting distant future trajectories of agents in a dynamic scene is challenging because the future trajectory of an agent is affected not only by their past trajectory but also the scene contexts. To tackle this problem, the authors propose a model based on recurrent neural networks, and a novel...

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Bibliographic Details
Main Authors: Dooseop Choi, Kyoungwook Min, Jeongdan Choi
Format: Article
Language:English
Published: Wiley 2020-08-01
Series:IET Computer Vision
Subjects:
future trajectory prediction
inverse reinforcement learning framework
dynamic scene
scene contexts
recurrent neural networks
encoder–decoder architecture
Online Access:https://doi.org/10.1049/iet-cvi.2019.0546

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