Exploiting deep residual networks for human action recognition from skeletal data Articles uri icon

authors

  • Huy Hieu, Pham
  • Khoudor, Louandi
  • Crouzil, Alain
  • Zegers, Pablo
  • VELASTIN CARROZA, SERGIO ALEJANDRO

publication date

  • May 2018

start page

  • 51

end page

  • 66

volume

  • 170

International Standard Serial Number (ISSN)

  • 1077-3142

Electronic International Standard Serial Number (EISSN)

  • 1090-235X

abstract

  • The computer vision community is currently focusing on solving action recognition problems in real videos, which contain thousands of samples with many challenges. In this process, Deep Convolutional Neural Networks (D-CNNs) have played a significant role in advancing the state-of-the-art in various vision-based action recognition systems. Recently, the introduction of residual connections in conjunction with a more traditional CNN model in a single architecture called Residual Network (ResNet) has shown impressive performance and great potential for image recognition tasks. In this paper, we investigate and apply deep ResNets for human action recognition using skeletal data provided by depth sensors. Firstly, the 3D coordinates of the human body joints carried in skeleton sequences are transformed into image-based representations and stored as RGB images. These color images are able to capture the spatial-temporal evolutions of 3D motions from skeleton sequences and can be efficiently learned by D-CNNs. We then propose a novel deep learning architecture based on ResNets to learn features from obtained color-based representations and classify them into action classes. The proposed method is evaluated on three challenging benchmark datasets including MSR Action 3D, KARD, and NTU-RGB + D datasets. Experimental results demonstrate that our method achieves state-of-the-art performance for all these benchmarks whilst requiring less computation resource. In particular, the proposed method surpasses previous approaches by a significant margin of 3.4% on MSR Action 3D dataset, 0.67% on KARD dataset, and 2.5% on NTU-RGB +D dataset.

subjects

  • Computer Science

keywords

  • 3d action recognition; deep residual networks; skeletal data