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The emergence of RGB-D sensors offered new possibilities for addressingcomplex artificial vision problems efficiently. Human posture recognition isamong these computer vision problems, with a wide range of applications such asambient assisted living and intelligent health care systems. In this context,our paper presents novel methods and ideas to design automatic posturerecognition systems using an RGB-D camera. More specifically, we introduce twosupervised methods to learn and recognize human postures using the main typesof visual data provided by an RGB-D camera. The first method is based onconvolutional features extracted from 2D images. Convolutional Neural Networks(CNNs) are trained to recognize human postures using transfer learning on RGBand depth images. Secondly, we propose to model the posture using the bodyjoint configuration in the 3D space. Posture recognition is then performedthrough SVM classification of 3D skeleton-based features. To evaluate theproposed methods, we created a challenging posture recognition dataset with aconsiderable variability regarding the acquisition conditions. The experimentalresults demonstrated comparable performances and high precision for bothmethods in recognizing human postures, with a slight superiority for theCNN-based method when applied on depth images. Moreover, the two approachesdemonstrated a high robustness to several perturbation factors, such as scaleand orientation change.

Posture recognition using an RGB-D camera : exploring 3D body modeling and deep learning approaches