Posture recognition using an RGB-D camera : exploring 3D body modeling and deep learning approaches
This work addresses posture recognition for applications like ambient assisted living and healthcare, presenting incremental improvements by combining existing deep learning and 3D modeling techniques.
The paper tackled human posture recognition using an RGB-D camera by introducing two supervised methods: one based on CNN features from 2D images and another using SVM classification of 3D skeleton features, achieving high precision and robustness with comparable performances, slightly favoring CNNs on depth images.
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.