Classification of human actions using pose-based features and stacked auto encoder

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RGB-D-based Human Motion Recognition with Deep Learning: A Survey (2017, methodology): “Most of available datasets require subjects to perform actions in a visible and restricted view to avoid occlusion, and this results in limited view data collection and less 29 SBU Kinect Interaction [149] Depth+ Skeleton PRNN Side Information 89.20% [130] Skeleton Co-occurrence +LSTM None 90.41% [1”
Semi-supervised Learning with Encoder-Decoder Recurrent Neural Networks: Experiments with Motion Capture Sequences (2015, background): “[22] use some joints distances metrics based on some domain knowledge (about actions found in a particular dataset) to create principled features that are then used as inputs to a neural classifier (pre-trained as a stacked auto-encoder).”