Fast 3D human body gesture recognition with multiple principal planes approximation

Chin Yi Cheng, Shyi Chyi Cheng, Jun-Wei Hsieh

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

In applying object reconstruction techniques to the problem of 3D shape approximation, we develop two new and powerful improvements to increase the robustness and accuracy of 3D human body gesture recognition. The first, the momentpreserving principal, solves the problem of 3D shape approximation with multiple surfaces by minimizing the shape reconstruction error. The second, we represents a surface with an affine-invariant surface descriptor for representing a 3D shape with the bag-of-words (BoW) model. The approach also aims at generating a time-ordered pose codebook to speed up the keyposes detection and improve precision. Our experiments demonstrate that these contributions make the 3D human body gesture recognition not only tractable but also highly accurate for our example application.

Original languageEnglish
Title of host publicationProceedings of IVCNZ 2014
Subtitle of host publicationThe 29th International Conference on Image and Vision Computing New Zealand
PublisherAssociation for Computing Machinery
Pages236-241
Number of pages6
ISBN (Electronic)9781450331845
DOIs
StatePublished - 19 Nov 2014
Event29th International Conference on Image and Vision Computing New Zealand, IVCNZ 2014 - Hamilton, New Zealand
Duration: 19 Nov 201421 Nov 2014

Publication series

NameACM International Conference Proceeding Series
Volume19-21-November-2014

Conference

Conference29th International Conference on Image and Vision Computing New Zealand, IVCNZ 2014
CountryNew Zealand
CityHamilton
Period19/11/1421/11/14

Keywords

  • 3D object reconstruction
  • 3D surface
  • Bag-of-words model
  • Gesture recognition
  • Moment-preserving principal

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