Co-Occurrence Feature Learning for Skeleton Based Action Recognition Using Regularized Deep LSTM Networks

Skeleton based action recognition distinguishes human actions using the trajectories of skeleton joints, which provide a very good representation for describing actions. Considering that recurrent neural networks (RNNs) with Long Short-Term Memory (LSTM) can learn feature representations and model long-term temporal dependencies automatically, we propose an end-to-end fully connected deep LSTM network for skeleton based action recognition. Inspired by the observation that the co-occurrences of the joints intrinsically characterize human actions, we take the skeleton as the input at each time slot and introduce a novel regularization scheme to learn the co-occurrence features of skeleton joints. To train the…

• MR
  Microsoft Research
• CA
  Chinese Academy of Sciences
• MR
  Microsoft Research Asia
• IO
  Institute of Automation, Chinese Academy of Sciences