Skeleton-Based Human Action Recognition With Global Context-Aware Attention LSTM Networks

Human action recognition in 3D skeleton sequences has attracted a lot of research attention. Recently, long short-term memory (LSTM) networks have shown promising performance in this task due to their strengths in modeling the dependencies and dynamics in sequential data. As not all skeletal joints are informative for action recognition, and the irrelevant joints often bring noise which can degrade the performance, we need to pay more attention to the informative ones. However, the original LSTM network does not have explicit attention ability. In this paper, we propose a new class of LSTM network, global context-aware attention LSTM, for skeleton-based action recognition, which is capable of selectively…

• NR
  National Research Foundation Singapore
• NN
  National Natural Science Foundation of China

  Awards: U1611461, 61661146005
• NK
  National Key Research and Development Program of China

  Award: 2016YFB1001501