Embedded Features for 1D CNN-based Action Recognition on Depth Maps

In this paper we present an algorithm for human action recognition using only depth maps. A convolutional autoencoder and Siamese neural network are trained to learn embedded features, encapsulating the content of single depth maps. Afterwards, statistical features and multichannel 1D CNN features are extracted on multivariate time-series of such embedded features to represent actions on depth map sequences. The action recognition is achieved by voting in an ensemble of one-vs-all weak classifiers. We demonstrate experimentally that the proposed algorithm achieves competitive results on UTD-MHAD dataset and outperforms by a large margin the best algorithms on 3D Human-Object Interaction Set (SYSU 3DHOI).