An image representation of skeletal data for action recognition using convolutional neural networks
Ioannis Vernikos, Eirini Mathe, Antonios E. Papadakis, Evaggelos Spyrou, Phivos Mylonas · 2019
In this paper we present preliminary results of an approach for understanding human actions, based on a novel 2D image representation for 3D skeletal data. More specifically, motion information for human skeletal joints is transformed to a pseudo-colored image. A Convolutional Neural Network is then used for classification. Our approach is evaluated for actions that may be used in an ambient assisted living scenario.