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Proceedings Paper

Hierarchical human action recognition around sleeping using obscured posture information
Author(s): Yuta Kudo; Takehiko Sashida; Yoshimitsu Aoki
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Paper Abstract

This paper presents a new approach for human action recognition around sleeping with the human body parts locations and the positional relationship between human and sleeping environment. Body parts are estimated from the depth image obtained by a time-of-flight (TOF) sensor using oriented 3D normal vector. Issues in action recognition of sleeping situation are the demand of availability in darkness, and hiding of the human body by duvets. Therefore, the extraction of image features is difficult since color and edge features are obscured by covers. Thus, first in our method, positions of four parts of the body (head, torso, thigh, and lower leg) are estimated by using the shape model of bodily surface constructed by oriented 3D normal vector. This shape model can represent the surface shape of rough body, and is effective in robust posture estimation of the body hidden with duvets. Then, action descriptor is extracted from the position of each body part. The descriptor includes temporal variation of each part of the body and spatial vector of position of the parts and the bed. Furthermore, this paper proposes hierarchical action classes and classifiers to improve the indistinct action classification. Classifiers are composed of two layers, and recognize human action by using the action descriptor. First layer focuses on spatial descriptor and classifies action roughly. Second layer focuses on temporal descriptor and classifies action finely. This approach achieves a robust recognition of obscured human by using the posture information and the hierarchical action recognition.

Paper Details

Date Published: 30 April 2015
PDF: 6 pages
Proc. SPIE 9534, Twelfth International Conference on Quality Control by Artificial Vision 2015, 953418 (30 April 2015); doi: 10.1117/12.2182870
Show Author Affiliations
Yuta Kudo, Keio Univ. (Japan)
Takehiko Sashida, Konica Minolta, Inc. (Japan)
Yoshimitsu Aoki, Keio Univ. (Japan)

Published in SPIE Proceedings Vol. 9534:
Twelfth International Conference on Quality Control by Artificial Vision 2015
Fabrice Meriaudeau; Olivier Aubreton, Editor(s)

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