Human action recognition based on action forests model using kinect camera

Chi Hung Chuan, Ying Nong Chen, Kuo Chin Fan

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

7 Scopus citations

Abstract

Human action recognition is one of the most important issues in computer vision. In this paper, the main idea is to design a general approach to recognize the human behavior. This approach is based on a pre-collected action database, which extracted by the depth images and forms the sequences of skeletons, and trained by the proposed Action Forests (AF) model. AF extends the random forest algorithm by using different decision functions to fit the skeletal features in 3D space. The system achieves the real-time classification result without the limitation of background and camera position. In the experiments, we collected several human behavior with single-character actions and two-character interactions to train the AF model. The skeleton features were retrieved from the depth sensor Kinect. We investigated the effect of several training parameters in AF. In conclusion, AF can learn the skeletal features efficiently and runs at 30 frames per second on action classification with high accuracy.

Original languageEnglish
Title of host publicationProceedings - IEEE 30th International Conference on Advanced Information Networking and Applications Workshops, WAINA 2016
EditorsAntonio J. Jara, Makoto Takizawa, Yann Bocchi, Leonard Barolli, Tomoya Enokido
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages914-917
Number of pages4
ISBN (Electronic)9781509018574
DOIs
StatePublished - 17 May 2016
Event30th IEEE International Conference on Advanced Information Networking and Applications Workshops, WAINA 2016 - Crans-Montana, Switzerland
Duration: 23 Mar 201625 Mar 2016

Publication series

NameProceedings - IEEE 30th International Conference on Advanced Information Networking and Applications Workshops, WAINA 2016

Conference

Conference30th IEEE International Conference on Advanced Information Networking and Applications Workshops, WAINA 2016
Country/TerritorySwitzerland
CityCrans-Montana
Period23/03/1625/03/16

Keywords

  • Action forests
  • Action recognition
  • Depth image
  • Kinect
  • Random forests

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