TAD-Net: An Approach for Realtime Action Detection Based on TCN and GCN in Digital Twin Shop-floor
DO I(10.12688/digitaltwin.17408.1)
We proposed a real-time detection approach for shop-floor production action, this approach took the sequence data of continuous human skeleton joints sequence as input, reconstructed the Joint Classification-Regression Recurrent Neural Networks (JCR-RNN) based on Temporal Convolution Network (TCN) and Graph Convolution Network (GCN), constructed our Temporal Action Detection Net (TAD-Net), realized real-time shop-floor production action detection
