Posture Recognition Using an RGB-D Camera: Exploring 3D Body Modeling and Deep Learning Approaches

Mohamed El Amine Elforaici1, Ismail Chaaraoui1, Wassim Bouachir1, Youssef Ouakrim1, Neila Mezghani2

  • 1TÉLUQ University
  • 2TELUQ university



Poster Session


10:00 - 17:00 | Mon 29 Oct | Foyer | A1P-E

Cognitive Computing & Deep Learning in Life Sciences

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This paper aims to explore the promising possibilities offered by an RGB-D camera to address the automatic posture recognition problem. For this purpose, we designed two methods using different types of visual data provided by an RGB-D camera. The first method is based on CNN classification of 2D images, while the second uses 3D body modeling to compute and classify high level features. The experimental results demonstrated comparable performances and high precision for both methods, with a slight superiority for the CNN-based method when applied on depth images.

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