  The emergence of RGB-D sensors offered new possibilities for addressing
complex artificial vision problems efficiently. Human posture recognition is
among these computer vision problems, with a wide range of applications such as
ambient assisted living and intelligent health care systems. In this context,
our paper presents novel methods and ideas to design automatic posture
recognition systems using an RGB-D camera. More specifically, we introduce two
supervised methods to learn and recognize human postures using the main types
of visual data provided by an RGB-D camera. The first method is based on
convolutional features extracted from 2D images. Convolutional Neural Networks
(CNNs) are trained to recognize human postures using transfer learning on RGB
and depth images. Secondly, we propose to model the posture using the body
joint configuration in the 3D space. Posture recognition is then performed
through SVM classification of 3D skeleton-based features. To evaluate the
proposed methods, we created a challenging posture recognition dataset with a
considerable variability regarding the acquisition conditions. The experimental
results demonstrated comparable performances and high precision for both
methods in recognizing human postures, with a slight superiority for the
CNN-based method when applied on depth images. Moreover, the two approaches
demonstrated a high robustness to several perturbation factors, such as scale
and orientation change.
