Unsupervised Human Action Recognition with Skeletal Graph Laplacian and Self-Supervised Viewpoints Invariance

The 32nd British Machine Vision Conference (BMVC) 2021 This paper presents a novel end-to-end method for the problem of skeleton-based unsupervised human action recognition. We propose a new architecture with a convolutional autoencoder that uses graph Laplacian regularization to model the skeletal...

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Bibliographic Details
Main Authors Paoletti, Giancarlo, Cavazza, Jacopo, Beyan, Cigdem, Del Bue, Alessio
Format Journal Article
LanguageEnglish
Published 21.04.2022
Subjects
Computer Science - Computer Vision and Pattern Recognition
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DOI10.48550/arxiv.2204.10312

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Summary:The 32nd British Machine Vision Conference (BMVC) 2021 This paper presents a novel end-to-end method for the problem of skeleton-based unsupervised human action recognition. We propose a new architecture with a convolutional autoencoder that uses graph Laplacian regularization to model the skeletal geometry across the temporal dynamics of actions. Our approach is robust towards viewpoint variations by including a self-supervised gradient reverse layer that ensures generalization across camera views. The proposed method is validated on NTU-60 and NTU-120 large-scale datasets in which it outperforms all prior unsupervised skeleton-based approaches on the cross-subject, cross-view, and cross-setup protocols. Although unsupervised, our learnable representation allows our method even to surpass a few supervised skeleton-based action recognition methods. The code is available in: www.github.com/IIT-PAVIS/UHAR_Skeletal_Laplacian
DOI:10.48550/arxiv.2204.10312