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International Journal of Machine Learning and Cybernetics
Erschienen in: International Journal of Machine Learning and Cybernetics 12/2020

05.05.2020 | Original Article

View-independent representation with frame interpolation method for skeleton-based human action recognition

verfasst von: Yingguo Jiang, Jun Xu, Tong Zhang

Erschienen in: International Journal of Machine Learning and Cybernetics | Ausgabe 12/2020

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Abstract

Human action recognition is an important branch of computer vision science. It is a challenging task based on skeletal data because of joints’ complex spatiotemporal information. In this work, we propose a method for action recognition, which consists of three parts: view-independent representation, frame interpolation, and combined model. First, the action sequence becomes view-independent representations independent of the view. Second, when judgment conditions are met, differentiated frame interpolations are used to expand the temporal dimensional information. Then, a combined model is adopted to extract these representation features and classify actions. Experimental results on two multi-view benchmark datasets Northwestern-UCLA and NTU RGB+D demonstrate the effectiveness of our complete method. Although using only one type of action feature and a simple architecture combined model, our complete method still outperforms most of the referential state-of-the-art methods and has strong robustness.
Vorheriger Artikel Fast feature selection for interval-valued data through kernel density estimation entropy
Nächster Artikel Attentive convolutional gated recurrent network: a contextual model to sentiment analysis

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Metadaten
Titel
View-independent representation with frame interpolation method for skeleton-based human action recognition
verfasst von
Yingguo Jiang
Jun Xu
Tong Zhang
Publikationsdatum
05.05.2020
Verlag
Springer Berlin Heidelberg
Erschienen in
International Journal of Machine Learning and Cybernetics / Ausgabe 12/2020
Print ISSN: 1868-8071
Elektronische ISSN: 1868-808X
DOI
https://doi.org/10.1007/s13042-020-01132-4

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