Human Action Recognition on Raw Depth Maps

Abstract

We propose an effective framework for human action recognition on raw depth maps. We leverage a convolutional autoencoder to extract on sequences of deep maps the frame-features that are then fed to a 1D-CNN responsible for embedding action features. A Siamese neural network trained on repre-sentative single depth map for each sequence extracts features, which are then processed by shapelets algorithm to extract action features. These features are then concatenated with features extracted by a BiLSTM with TimeDistributed wrapper. Given the learned individual models on such features we perform a selection of a subset of models. We demonstrate experimentally that on SYSU 3DHOI dataset the proposed algorithm outperforms considerably all recent algorithms including skeleton-based ones.