Learning latent global network for Skeleton-based action prediction
AuthorKe, Q; Bennamoun, M; Rahmani, H; An, S; Sohel, F; Boussaid, F
Source TitleIEEE Transactions on Image Processing
University of Melbourne Author/sKe, Qiuhong
AffiliationComputing and Information Systems
Document TypeJournal Article
CitationsKe, Q., Bennamoun, M., Rahmani, H., An, S., Sohel, F. & Boussaid, F. (2020). Learning latent global network for Skeleton-based action prediction. IEEE Transactions on Image Processing, 29, pp.959-970. https://doi.org/10.1109/TIP.2019.2937757.
Access StatusAccess this item via the Open Access location
Open Access URLAccepted version
Human actions represented with 3D skeleton sequences are robust to clustered backgrounds and illumination changes. In this paper, we investigate skeleton-based action prediction, which aims to recognize an action from a partial skeleton sequence that contains incomplete action information. We propose a new Latent Global Network based on adversarial learning for action prediction. We demonstrate that the proposed network provides latent long-term global information that is complementary to the local action information of the partial sequences and helps improve action prediction. We show that action prediction can be improved by combining the latent global information with the local action information. We test the proposed method on three challenging skeleton datasets and report state-of-the-art performance.
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