2D Skeleton-Based Action Recognition via Two-Branch Stacked LSTM-RNNs
Action recognition in video sequences is an inter-esting field for many computer vision applications, includingbehaviour analysis, event recognition, and video surveillance.In this work, a method based on 2D skeleton and two-branchstacked Recurrent Neural Networks (RNNs) with Long Short-Term Memory (LSTM) cells is proposed. Unlike 3D skeletons,usually generated by RGB-D cameras, the 2D skeletons adoptedin this work are reconstructed starting from RGB video streams,therefore allowing the use of the proposed approach in bothindoor and outdoor environments.