A background modeling method for videos based on weighted statistical classification

In the field of intelligent video surveillance, foreground detection, moving target tracking and target recognition are the key technologies. They play an important role in target behavior analysis and understanding. In this paper a background modeling method based on weighted statistical classifica...

Full description

Saved in:
Bibliographic Details
Main Authors: Jiang-Qin Gui, Jian-Wei Zhang, Li-Qiang Hu, Ye Wen-Zhong, Yong-Hui Li, Dong-Fa Gao
Format: Conference Proceeding
Language:English
Subjects:
Abstracts
background modeling
Foreground detection
Gaussian mixture model
no-parameter background modeling
Online Access:Request full text
Tags: Add Tag
No Tags, Be the first to tag this record!
cited_by
cites
container_end_page 462
container_issue
container_start_page 456
container_title
container_volume 1
creator Jiang-Qin Gui
Jian-Wei Zhang
Li-Qiang Hu
Ye Wen-Zhong
Yong-Hui Li
Dong-Fa Gao
description In the field of intelligent video surveillance, foreground detection, moving target tracking and target recognition are the key technologies. They play an important role in target behavior analysis and understanding. In this paper a background modeling method based on weighted statistical classification is proposed. As a non-parametric background model, it uses several state categories to express multiple states of a background pixel. It does not require the background pixels to obey Gaussian distribution and needs no training. The weights are updated according to the matching history of the background pixel. The background state is determined by a threshold. Experiment results show that it obtains excellent detection results and real-time detection speed in complex scenes.
doi_str_mv 10.1109/ICMLC.2013.6890508
format conference_proceeding
fullrecord <record><control><sourceid>ieee_CHZPO</sourceid><recordid>TN_cdi_ieee_primary_6890508</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><ieee_id>6890508</ieee_id><sourcerecordid>6890508</sourcerecordid><originalsourceid>FETCH-LOGICAL-i175t-c6f9b99b3c79036c5916992e7df7024a1714c39095ea67ce9774aef7e536fe6b3</originalsourceid><addsrcrecordid>eNotkM1KxDAUhSMoOI7zArrJC7QmTZv0Lofiz0DFzQjuhjS56UTbRpqo-PYWnLM53-LjLA4hN5zlnDO42zXPbZMXjItc1sAqVp-RK14qAFZIVp-TVcEly7gQb5dkE-M7W6LKsga-Ivst7bT56OfwNVk6BouDn3o6YjoGS12Y6be3GOJiRbQ0TPQHfX9MC8ekk4_JGz1QM-gYvVs4-TBdkwunh4ibU6_J68P9vnnK2pfHXbNtM89VlTIjHXQAnTAKmJCmAi4BClTWKVaUmiteGgEMKtRSGQSlSo1OYSWkQ9mJNbn93_WIePic_ajn38PpBPEHeu9Rlg</addsrcrecordid><sourcetype>Publisher</sourcetype><iscdi>true</iscdi><recordtype>conference_proceeding</recordtype></control><display><type>conference_proceeding</type><title>A background modeling method for videos based on weighted statistical classification</title><source>IEEE Xplore All Conference Series</source><creator>Jiang-Qin Gui ; Jian-Wei Zhang ; Li-Qiang Hu ; Ye Wen-Zhong ; Yong-Hui Li ; Dong-Fa Gao</creator><creatorcontrib>Jiang-Qin Gui ; Jian-Wei Zhang ; Li-Qiang Hu ; Ye Wen-Zhong ; Yong-Hui Li ; Dong-Fa Gao</creatorcontrib><description>In the field of intelligent video surveillance, foreground detection, moving target tracking and target recognition are the key technologies. They play an important role in target behavior analysis and understanding. In this paper a background modeling method based on weighted statistical classification is proposed. As a non-parametric background model, it uses several state categories to express multiple states of a background pixel. It does not require the background pixels to obey Gaussian distribution and needs no training. The weights are updated according to the matching history of the background pixel. The background state is determined by a threshold. Experiment results show that it obtains excellent detection results and real-time detection speed in complex scenes.</description><identifier>ISSN: 2160-133X</identifier><identifier>EISBN: 1479902608</identifier><identifier>EISBN: 9781479902606</identifier><identifier>DOI: 10.1109/ICMLC.2013.6890508</identifier><language>eng</language><publisher>IEEE</publisher><subject>Abstracts ; background modeling ; Foreground detection ; Gaussian mixture model ; no-parameter background modeling</subject><ispartof>2013 International Conference on Machine Learning and Cybernetics, 2013, Vol.1, p.456-462</ispartof><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/6890508$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>309,310,780,784,789,790,23930,23931,25140,27925,54555,54932</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/6890508$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Jiang-Qin Gui</creatorcontrib><creatorcontrib>Jian-Wei Zhang</creatorcontrib><creatorcontrib>Li-Qiang Hu</creatorcontrib><creatorcontrib>Ye Wen-Zhong</creatorcontrib><creatorcontrib>Yong-Hui Li</creatorcontrib><creatorcontrib>Dong-Fa Gao</creatorcontrib><title>A background modeling method for videos based on weighted statistical classification</title><title>2013 International Conference on Machine Learning and Cybernetics</title><addtitle>ICMLC</addtitle><description>In the field of intelligent video surveillance, foreground detection, moving target tracking and target recognition are the key technologies. They play an important role in target behavior analysis and understanding. In this paper a background modeling method based on weighted statistical classification is proposed. As a non-parametric background model, it uses several state categories to express multiple states of a background pixel. It does not require the background pixels to obey Gaussian distribution and needs no training. The weights are updated according to the matching history of the background pixel. The background state is determined by a threshold. Experiment results show that it obtains excellent detection results and real-time detection speed in complex scenes.</description><subject>Abstracts</subject><subject>background modeling</subject><subject>Foreground detection</subject><subject>Gaussian mixture model</subject><subject>no-parameter background modeling</subject><issn>2160-133X</issn><isbn>1479902608</isbn><isbn>9781479902606</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2013</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><recordid>eNotkM1KxDAUhSMoOI7zArrJC7QmTZv0Lofiz0DFzQjuhjS56UTbRpqo-PYWnLM53-LjLA4hN5zlnDO42zXPbZMXjItc1sAqVp-RK14qAFZIVp-TVcEly7gQb5dkE-M7W6LKsga-Ivst7bT56OfwNVk6BouDn3o6YjoGS12Y6be3GOJiRbQ0TPQHfX9MC8ekk4_JGz1QM-gYvVs4-TBdkwunh4ibU6_J68P9vnnK2pfHXbNtM89VlTIjHXQAnTAKmJCmAi4BClTWKVaUmiteGgEMKtRSGQSlSo1OYSWkQ9mJNbn93_WIePic_ajn38PpBPEHeu9Rlg</recordid><startdate>201307</startdate><enddate>201307</enddate><creator>Jiang-Qin Gui</creator><creator>Jian-Wei Zhang</creator><creator>Li-Qiang Hu</creator><creator>Ye Wen-Zhong</creator><creator>Yong-Hui Li</creator><creator>Dong-Fa Gao</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>201307</creationdate><title>A background modeling method for videos based on weighted statistical classification</title><author>Jiang-Qin Gui ; Jian-Wei Zhang ; Li-Qiang Hu ; Ye Wen-Zhong ; Yong-Hui Li ; Dong-Fa Gao</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i175t-c6f9b99b3c79036c5916992e7df7024a1714c39095ea67ce9774aef7e536fe6b3</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2013</creationdate><topic>Abstracts</topic><topic>background modeling</topic><topic>Foreground detection</topic><topic>Gaussian mixture model</topic><topic>no-parameter background modeling</topic><toplevel>online_resources</toplevel><creatorcontrib>Jiang-Qin Gui</creatorcontrib><creatorcontrib>Jian-Wei Zhang</creatorcontrib><creatorcontrib>Li-Qiang Hu</creatorcontrib><creatorcontrib>Ye Wen-Zhong</creatorcontrib><creatorcontrib>Yong-Hui Li</creatorcontrib><creatorcontrib>Dong-Fa Gao</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan All Online (POP All Online) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Electronic Library (IEL)</collection><collection>IEEE Proceedings Order Plans (POP All) 1998-Present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Jiang-Qin Gui</au><au>Jian-Wei Zhang</au><au>Li-Qiang Hu</au><au>Ye Wen-Zhong</au><au>Yong-Hui Li</au><au>Dong-Fa Gao</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>A background modeling method for videos based on weighted statistical classification</atitle><btitle>2013 International Conference on Machine Learning and Cybernetics</btitle><stitle>ICMLC</stitle><date>2013-07</date><risdate>2013</risdate><volume>1</volume><spage>456</spage><epage>462</epage><pages>456-462</pages><issn>2160-133X</issn><eisbn>1479902608</eisbn><eisbn>9781479902606</eisbn><abstract>In the field of intelligent video surveillance, foreground detection, moving target tracking and target recognition are the key technologies. They play an important role in target behavior analysis and understanding. In this paper a background modeling method based on weighted statistical classification is proposed. As a non-parametric background model, it uses several state categories to express multiple states of a background pixel. It does not require the background pixels to obey Gaussian distribution and needs no training. The weights are updated according to the matching history of the background pixel. The background state is determined by a threshold. Experiment results show that it obtains excellent detection results and real-time detection speed in complex scenes.</abstract><pub>IEEE</pub><doi>10.1109/ICMLC.2013.6890508</doi><tpages>7</tpages></addata></record>
fulltext fulltext_linktorsrc
identifier ISSN: 2160-133X
ispartof 2013 International Conference on Machine Learning and Cybernetics, 2013, Vol.1, p.456-462
issn 2160-133X
language eng
recordid cdi_ieee_primary_6890508
source IEEE Xplore All Conference Series
subjects Abstracts
background modeling
Foreground detection
Gaussian mixture model
no-parameter background modeling
title A background modeling method for videos based on weighted statistical classification
url http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-07T20%3A40%3A48IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-ieee_CHZPO&rft_val_fmt=info:ofi/fmt:kev:mtx:book&rft.genre=proceeding&rft.atitle=A%20background%20modeling%20method%20for%20videos%20based%20on%20weighted%20statistical%20classification&rft.btitle=2013%20International%20Conference%20on%20Machine%20Learning%20and%20Cybernetics&rft.au=Jiang-Qin%20Gui&rft.date=2013-07&rft.volume=1&rft.spage=456&rft.epage=462&rft.pages=456-462&rft.issn=2160-133X&rft_id=info:doi/10.1109/ICMLC.2013.6890508&rft.eisbn=1479902608&rft.eisbn_list=9781479902606&rft_dat=%3Cieee_CHZPO%3E6890508%3C/ieee_CHZPO%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-i175t-c6f9b99b3c79036c5916992e7df7024a1714c39095ea67ce9774aef7e536fe6b3%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_id=info:pmid/&rft_ieee_id=6890508&rfr_iscdi=true