Feature integration with random forests for real-time human activity recognition

Hirokatsu Kataoka, Kiyoshi Hashimoto, Yoshimitsu Aoki

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This paper presents an approach for real-time human activity recognition. Three different kinds of features (flow, shape, and a keypoint-based feature) are applied in activity recognition. We use random forests for feature integration and activity classification. A forest is created at each feature that performs as a weak classifier. The international classification of functioning, disability and health (ICF) proposed by WHO is applied in order to set the novel definition in activity recognition. Experiments on human activity recognition using the proposed framework show - 99.2% (Weizmann action dataset), 95.5% (KTH human actions dataset), and 54.6% (UCF50 dataset) recognition accuracy with a real-time processing speed. The feature integration and activity-class definition allow us to accomplish high-accuracy recognition match for the state-of-the-art in real-time.

Original languageEnglish
Title of host publicationSeventh International Conference on Machine Vision, ICMV 2014
EditorsBranislav Vuksanovic, Jianhong Zhou, Antanas Verikas, Petia Radeva
PublisherSPIE
ISBN (Electronic)9781628415605
DOIs
Publication statusPublished - 2015 Jan 1
Event7th International Conference on Machine Vision, ICMV 2014 - Milan, Italy
Duration: 2014 Nov 192014 Nov 21

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
Volume9445
ISSN (Print)0277-786X
ISSN (Electronic)1996-756X

Other

Other7th International Conference on Machine Vision, ICMV 2014
CountryItaly
CityMilan
Period14/11/1914/11/21

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Keywords

  • Activity Recognition
  • Feature Integration
  • Random Forests

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering

Cite this

Kataoka, H., Hashimoto, K., & Aoki, Y. (2015). Feature integration with random forests for real-time human activity recognition. In B. Vuksanovic, J. Zhou, A. Verikas, & P. Radeva (Eds.), Seventh International Conference on Machine Vision, ICMV 2014 [944506] (Proceedings of SPIE - The International Society for Optical Engineering; Vol. 9445). SPIE. https://doi.org/10.1117/12.2181201