Skip to Main content Skip to Navigation
Conference papers

3-D skeleton joints-based action recognition using covariance descriptors on discrete spherical harmonics transform

Adnan Al Alwani 1 Youssef Chahir 1
1 Equipe Image - Laboratoire GREYC - UMR6072
GREYC - Groupe de Recherche en Informatique, Image, Automatique et Instrumentation de Caen
Abstract : In this paper, we explore a new method for skeleton-based human action recognition. First, the normalized angles of local joints are extracted and Spherical Harmonics Transform (SHT) can then be used to explicitly model the angular skeleton by projecting the spherical angles onto unit sphere basis. This enables that the skeleton representation can be decomposed into a basis functions. We adopt the spatiotemporal covariance matrix of the spherical harmonic to capture joints orientations over the human action sequence. Thus, the co-variance coefficients of joints are used as a discriminative de-scriptor for the sequence. We validate the proposed method using Extreme Learning Machine (ELM) classifier and recent published 3D action datasets. Experimental results show that our method performs better than many classical methods.
Complete list of metadatas

Cited literature [23 references]  Display  Hide  Download

https://hal.archives-ouvertes.fr/hal-01168436
Contributor : Youssef Chahir <>
Submitted on : Monday, June 25, 2018 - 6:28:02 PM
Last modification on : Thursday, November 14, 2019 - 3:44:03 PM
Document(s) archivé(s) le : Wednesday, September 26, 2018 - 3:01:20 PM

File

icip2015.pdf
Files produced by the author(s)

Identifiers

  • HAL Id : hal-01168436, version 1

Citation

Adnan Al Alwani, Youssef Chahir. 3-D skeleton joints-based action recognition using covariance descriptors on discrete spherical harmonics transform. International Conference on Image Processing (ICIP 2015), IEEE, Sep 2015, Québec, Canada. ⟨hal-01168436⟩

Share

Metrics

Record views

175

Files downloads

99