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Paper Abstract and Keywords
Presentation 2017-05-26 12:00
Background Modeling based on Gaussian Mixture Model using Spatial Features
Kan Zheng, Toshio Kondo, Yuki Fukazawa, Takahiro Sasaki (Mie Univ.) SIP2017-24 IE2017-24 PRMU2017-24 MI2017-24
Abstract (in Japanese) (See Japanese page) 
(in English) Many methods for detecting a moving object from surveillance video using a background model have been proposed. Mixed Gaussian distribution is widely used to construct background models, but it can not cope well with scenes due to the frequent background changes. In this paper, we proposed a method with high followability to background change by using spatial information of pixels as feature, constructing a background model with mixed Gaussian distribution at multilevel. The results of the proposed method were compared with the Ground Truth of the data set and evaluated by the F - measure method, and it was confirmed that the precision rate and the recall rate of the proposed method exceed the conventional methods such as Gaussian Mixture Model (GMM) and Local Binary Pattern (LBP).
Keyword (in Japanese) (See Japanese page) 
(in English) Background model / Moving object detection / Gaussian Mixture Model / Spatial information / / / /  
Reference Info. IEICE Tech. Rep., vol. 117, no. 49, PRMU2017-24, pp. 125-130, May 2017.
Paper # PRMU2017-24 
Date of Issue 2017-05-18 (SIP, IE, PRMU, MI) 
ISSN Print edition: ISSN 0913-5685    Online edition: ISSN 2432-6380
Copyright
and
reproduction		 All rights are reserved and no part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Notwithstanding, instructors are permitted to photocopy isolated articles for noncommercial classroom use without fee. (License No.: 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034)
Download PDF SIP2017-24 IE2017-24 PRMU2017-24 MI2017-24

Conference Information
Committee PRMU IE MI SIP  
Conference Date 2017-05-25 - 2017-05-26 
Place (in Japanese) (See Japanese page) 
Place (in English)  
Topics (in Japanese) (See Japanese page) 
Topics (in English)  
Paper Information
Registration To PRMU 
Conference Code 2017-05-PRMU-IE-MI-SIP 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Background Modeling based on Gaussian Mixture Model using Spatial Features 
Sub Title (in English)  
Keyword(1) Background model  
Keyword(2) Moving object detection  
Keyword(3) Gaussian Mixture Model  
Keyword(4) Spatial information  
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1st Author's Name Kan Zheng  
1st Author's Affiliation Mie University (Mie Univ.)
2nd Author's Name Toshio Kondo  
2nd Author's Affiliation Mie University (Mie Univ.)
3rd Author's Name Yuki Fukazawa  
3rd Author's Affiliation Mie University (Mie Univ.)
4th Author's Name Takahiro Sasaki  
4th Author's Affiliation Mie University (Mie Univ.)
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Speaker Author-1 
Date Time 2017-05-26 12:00:00 
Presentation Time 30 minutes 
Registration for PRMU 
Paper # SIP2017-24, IE2017-24, PRMU2017-24, MI2017-24 
Volume (vol) vol.117 
Number (no) no.47(SIP), no.48(IE), no.49(PRMU), no.50(MI) 
Page pp.125-130 
#Pages 6 
Date of Issue 2017-05-18 (SIP, IE, PRMU, MI) 

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