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Proceedings Paper

Feature integration with random forests for real-time human activity recognition
Author(s): Hirokatsu Kataoka; Kiyoshi Hashimoto; Yoshimitsu Aoki
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Paper 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.

Paper Details

Date Published: 14 February 2015
PDF: 5 pages
Proc. SPIE 9445, Seventh International Conference on Machine Vision (ICMV 2014), 944506 (14 February 2015); doi: 10.1117/12.2181201
Show Author Affiliations
Hirokatsu Kataoka, Keio Univ. (Japan)
Kiyoshi Hashimoto, Keio Univ. (Japan)
Yoshimitsu Aoki, Keio Univ. (Japan)

Published in SPIE Proceedings Vol. 9445:
Seventh International Conference on Machine Vision (ICMV 2014)
Antanas Verikas; Branislav Vuksanovic; Petia Radeva; Jianhong Zhou, Editor(s)

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