Learning Latent Global Network for skeleton-based action prediction

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

Research output: Contribution to journalArticleResearchpeer-review

46 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)959-970
Number of pages12
JournalIEEE Transactions on Image Processing
Publication statusPublished - 2 Sept 2020
Externally publishedYes


  • adversarial learning
  • convolutional neural networks
  • Skeleton-based action prediction

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