Human Posture Recognition Based on Images Captured by the Kinect Sensor

NCUIR > College of Electrical Engineering & Computer Science > Department of Electrical Engineering > journal & Dissertation > Item 987654321/107436

Please use this identifier to cite or link to this item: https://ir.lib.ncu.edu.tw/handle/987654321/107436

Title:	Human Posture Recognition Based on Images Captured by the Kinect Sensor
Authors:	王文俊;Wang, Wen-June;Chang, Jun-Wei;Haung, Shih-Fu;Wang, Rong-Jyue
Contributors:	資訊電機學院電機工程學系
Keywords:	Center of gravity;Contours;Equipment and supplies;Fourier transforms;Human mechanics;Image processing;Methods;Motion capture;Neural networks;Object recognition;Object recognition (Computers);Older people;Pattern recognition;Portable computers;Posture;Recognition;Sensors;Shape;Studies;Subtraction;Vector quantization
Date:	2016-03-15
Issue Date:	2026-04-23 14:12:54 (UTC+8)
Publisher:	SAGE Publications Inc.;London, England: SAGE Publications
Abstract:	摘要： In this paper we combine several image processing techniques with the depth images captured by a Kinect sensor to successfully recognize the five distinct human postures of sitting, standing, stooping, kneeling, and lying. The proposed recognition procedure first uses background subtraction on the depth image to extract a silhouette contour of a human. Then, a horizontal projection of the silhouette contour is employed to ascertain whether or not the human is kneeling. If the figure is not kneeling, the star skeleton technique is applied to the silhouette contour to obtain its feature points. We can then use the feature points together with the centre of gravity to calculate the feature vectors and depth values of the body. Next, we input the feature vectors and the depth values into a pre-trained LVQ (learning vector quantization) neural network; the outputs of this will determine the postures of sitting (or standing), stooping, and lying. Lastly, if an output indicates sitting or standing, one further, similar feature identification technique is needed to confirm this output. Based on the results of many experiments, using the proposed method, the rate of successful recognition is higher than 97% in the test data, even though the subjects of the experiments may not have been facing the Kinect sensor and may have had different statures. The proposed method can be called a “hybrid recognition method”, as many techniques are combined in order to achieve a very high recognition rate paired with a very short processing time. 出版者： London, England: SAGE Publications 出版日期： 2016-03-15 出處： International Journal of Advanced Robotic Systems, 2016-03, Vol.13 (2) 資源來源： Sage Journals Open Access 版權： 2016 Author(s). Licensee InTech. 版權： COPYRIGHT 2016 Sage Publications Ltd. (UK) 版權： 2016. This work is published under http://creativecommons.org/licenses/by/3.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. 識別號： ISSN: 1729-8806 識別號： ISSN: 1729-8814 識別號： EISSN: 1729-8814 識別號： DOI: 10.5772/62163
Appears in Collections:	[Department of Electrical Engineering] journal & Dissertation

Files in This Item:

File	Description	Size	Format
index.html		0Kb	HTML	9	View/Open

社群 sharing

Loading...