Journal article

Learning latent global network for Skeleton-based action prediction

Qiuhong Ke, Mohammed Bennamoun, Hossein Rahmani, Senjian An, Ferdous Sohel, Farid Boussaid

IEEE Transactions on Image Processing | IEEE | Published : 2020


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 inf..

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Awarded by Australian Research Council

Funding Acknowledgements

This work was supported by the Australian Research Council under Grant DP150100294, Grant DP150104251, and Grant DE120102960. The associate editor coordinating the review of this manuscript and approving it for publication was Prof. Lisimachos P. Kondi.