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

Hirokatsu Kataoka, Kiyoshi Hashimoto, Yoshimitsu Aoki

研究成果: Conference contribution

抄録

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.

本文言語English
ホスト出版物のタイトルSeventh International Conference on Machine Vision, ICMV 2014
編集者Branislav Vuksanovic, Jianhong Zhou, Antanas Verikas, Petia Radeva
出版社SPIE
ISBN(電子版)9781628415605
DOI
出版ステータスPublished - 2015
イベント7th International Conference on Machine Vision, ICMV 2014 - Milan, Italy
継続期間: 2014 11 192014 11 21

出版物シリーズ

名前Proceedings of SPIE - The International Society for Optical Engineering
9445
ISSN(印刷版)0277-786X
ISSN(電子版)1996-756X

Other

Other7th International Conference on Machine Vision, ICMV 2014
国/地域Italy
CityMilan
Period14/11/1914/11/21

ASJC Scopus subject areas

  • 電子材料、光学材料、および磁性材料
  • 凝縮系物理学
  • コンピュータ サイエンスの応用
  • 応用数学
  • 電子工学および電気工学

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