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Erschienen in:
Fashion Captioning: Towards Generating Accurate Descriptions with Semantic Rewards Kapitel lesen Erstes Kapitel lesen

2020 | OriginalPaper | Buchkapitel

SimAug: Learning Robust Representations from Simulation for Trajectory Prediction

verfasst von : Junwei Liang, Lu Jiang, Alexander Hauptmann

Erschienen in: Computer Vision – ECCV 2020

Verlag: Springer International Publishing

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Abstract

This paper studies the problem of predicting future trajectories of people in unseen cameras of novel scenarios and views. We approach this problem through the real-data-free setting in which the model is trained only on 3D simulation data and applied out-of-the-box to a wide variety of real cameras. We propose a novel approach to learn robust representation through augmenting the simulation training data such that the representation can better generalize to unseen real-world test data. The key idea is to mix the feature of the hardest camera view with the adversarial feature of the original view. We refer to our method as SimAug. We show that SimAug achieves promising results on three real-world benchmarks using zero real training data, and state-of-the-art performance in the Stanford Drone and the VIRAT/ActEV dataset when using in-domain training data. Code and models are released at https://​next.​cs.​cmu.​edu/​simaug.

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Nächstes Kapitel ScribbleBox: Interactive Annotation Framework for Video Object Segmentation
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Metadaten
Titel
SimAug: Learning Robust Representations from Simulation for Trajectory Prediction
verfasst von
Junwei Liang
Lu Jiang
Alexander Hauptmann
Copyright-Jahr
2020
Buch
Computer Vision – ECCV 2020

Print ISBN: 978-3-030-58600-3

Electronic ISBN: 978-3-030-58601-0

Copyright-Jahr: 2020

DOI
https://doi.org/10.1007/978-3-030-58601-0_17

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