Recognizing human actions using 3D skeletal information and CNNs

Abstract

In this paper we present an approach for the recognition of human actions targeting at activities of daily living (ADLs). Skeletal information is used to create images capturing the motion of joints in the 3D space. These images are then transformed to the spectral domain using 4 well-known image transforms. A deep Convolutional Neural Network is trained on those images. Our approach is thoroughly evaluated using a well-known, publicly available challenging dataset and for a set of actions that resembles to common ADLs, covering both cross-view and cross-subject cases. © Springer Nature Switzerland AG 2019.