基于改进在线多示例学习算法的机器人目标跟踪

提出基于改进的在线多示例学习算法（Improved multiple instance learning,IMIL）的移动机器人目标跟踪方法.该方法利用射频识别系统（Radio frequency identification,RFID）粗定位IMIL算法的搜索区域,然后应用IMIL算法实现目标跟踪.该方法保证了机器人跟踪系统的连续性,解决了目标突然转弯时的跟踪问题.IMIL算法采用从低维空间提取的压缩特征描述包中示例,以降低算法耗时.通过最大化弱分类器与极大似然概率的内积,选择判别能力强的弱分类器,避免了弱分类器选择过程中多次计算包概率和示例概率,进一步提高算法的实时处理能力.计算包概率时该...

Full description

Saved in:
Bibliographic Details
Published in自动化学报 Vol. 40; no. 12; pp. 2916 - 2925
Main Author 王丽佳 贾松敏 李秀智 王爽
Format Journal Article
LanguageChinese
Published 2014
Subjects
压缩特征
射频识别系统
改进的在线多示例学习
目标跟踪
Online AccessGet full text

Cover

Abstract 提出基于改进的在线多示例学习算法（Improved multiple instance learning,IMIL）的移动机器人目标跟踪方法.该方法利用射频识别系统（Radio frequency identification,RFID）粗定位IMIL算法的搜索区域,然后应用IMIL算法实现目标跟踪.该方法保证了机器人跟踪系统的连续性,解决了目标突然转弯时的跟踪问题.IMIL算法采用从低维空间提取的压缩特征描述包中示例,以降低算法耗时.通过最大化弱分类器与极大似然概率的内积,选择判别能力强的弱分类器,避免了弱分类器选择过程中多次计算包概率和示例概率,进一步提高算法的实时处理能力.计算包概率时该算法平等对待各示例,保证概率高的示例对包概率的贡献度,克服跟踪漂移问题.跟踪过程中,结合当前跟踪结果与目标模板间的相似性分数在线实时调整分类器,提高了算法的自适应能力.最后将本文方法在视频和移动机器人上进行实验.实验结果表明,该方法在目标运动突变及外观改变时具有较强的鲁棒性和准确性,并满足系统的实时性要求.
AbstractList 提出基于改进的在线多示例学习算法（Improved multiple instance learning,IMIL）的移动机器人目标跟踪方法.该方法利用射频识别系统（Radio frequency identification,RFID）粗定位IMIL算法的搜索区域,然后应用IMIL算法实现目标跟踪.该方法保证了机器人跟踪系统的连续性,解决了目标突然转弯时的跟踪问题.IMIL算法采用从低维空间提取的压缩特征描述包中示例,以降低算法耗时.通过最大化弱分类器与极大似然概率的内积,选择判别能力强的弱分类器,避免了弱分类器选择过程中多次计算包概率和示例概率,进一步提高算法的实时处理能力.计算包概率时该算法平等对待各示例,保证概率高的示例对包概率的贡献度,克服跟踪漂移问题.跟踪过程中,结合当前跟踪结果与目标模板间的相似性分数在线实时调整分类器,提高了算法的自适应能力.最后将本文方法在视频和移动机器人上进行实验.实验结果表明,该方法在目标运动突变及外观改变时具有较强的鲁棒性和准确性,并满足系统的实时性要求.
Author 王丽佳 贾松敏 李秀智 王爽
AuthorAffiliation 北京工业大学电子信息与控制工程学院,北京100124 河北工业职业技术学院信息工程与自动化系,石家庄050091
Author_xml – sequence: 1
  fullname: 王丽佳 贾松敏 李秀智 王爽
BookMark eNrjYmDJy89LZWLgNLQwN9E1NDCyZGHgNDAyNdE1MTQ142DgLS7OTDIwNDcxtzQyNuBksH86f9eTXX3Ppux8sX_20zkrnu_a_3TJrOdLdj3Z1_107bInOxc8Xzf92eapz2e1PJuz6-nMFU927Xo-e92zBe0vts9_sWMVDwNrWmJOcSovlOZmUHJzDXH20E3OyM9LL8zMS48vKMrMTSyqjDczMzYysrQ0NzUmShEAOAFShA
ContentType Journal Article
DBID 2RA
92L
CQIGP
W92
~WA
DatabaseName 维普_期刊
中文科技期刊数据库-CALIS站点
中文科技期刊数据库-7.0平台
中文科技期刊数据库-工程技术
中文科技期刊数据库- 镜像站点
DatabaseTitleList
DeliveryMethod fulltext_linktorsrc
Discipline Engineering
DocumentTitleAlternate Person Following for Mobile Robot Using Improved Multiple Instance Learning
EISSN 1874-1029
EndPage 2925
ExternalDocumentID 663229975
GroupedDBID --K
-0Y
.~1
0R~
1B1
1~.
1~5
2B.
2C0
2RA
4.4
457
4G.
5GY
5VS
5XA
5XJ
7-5
71M
8P~
92H
92I
92L
AAIKJ
AALRI
AAQFI
AAXUO
ACGFS
ADEZE
ADTZH
AECPX
AEKER
AFTJW
AGHFR
AGYEJ
AITUG
AJOXV
ALMA_UNASSIGNED_HOLDINGS
BLXMC
CCEZO
CQIGP
CS3
CUBFJ
CW9
EBS
EFLBG
EJD
EO8
EO9
EP2
EP3
FDB
FEDTE
FNPLU
GBLVA
HVGLF
HZ~
IHE
J1W
JJJVA
M41
MO0
N9A
O-L
O9-
OAUVE
OZT
P-8
P-9
P2P
PC.
Q38
ROL
RPZ
SDF
SDG
SES
TCJ
TGT
U1G
U5S
W92
~WA
ID FETCH-chongqing_primary_6632299753
ISSN 0254-4156
IngestDate Wed Feb 14 10:33:50 EST 2024
IsPeerReviewed true
IsScholarly true
Issue 12
Language Chinese
LinkModel OpenURL
MergedId FETCHMERGED-chongqing_primary_6632299753
Notes Improved multiple instance learning （IMIL）, person following, radio frequency identification （RFID）, com-pressed features
11-2109/TP
WANG Li-Jia, JIA Song-Min, LI Xiu-Zhi, WANG Shuang
An improved multiple instance learning （IMIL） algorithm is proposed for person following with a mobile robot. In the tracking process, radio frequency identification （RFID） provides a searching area for the IMIL algorithm, which then successfully detects the person of interest. In IMIL, compressed features are extracted to describe instances of bags from the low dimensional space so as to reduce the time complexity in the operation. Then, the most discriminative weak classifiers are selected from the weak classifier pool by maximizing the inner product between the weak classifier and the log-likelihood function. The scheme avoids computing the bag probability and instance probability many times, which further reduces the computational time. To deal with the drift problem, the bag probability equally depends on each instance. Furthe
ParticipantIDs chongqing_primary_663229975
PublicationCentury 2000
PublicationDate 2014
PublicationDateYYYYMMDD 2014-01-01
PublicationDate_xml – year: 2014
  text: 2014
PublicationDecade 2010
PublicationTitle 自动化学报
PublicationTitleAlternate Acta Automatica Sinica
PublicationYear 2014
SSID ssib017479230
ssib001102911
ssib006576350
ssib051375349
ssib007293330
ssj0059721
ssib007290157
ssib023646446
ssib005904210
Score 4.1728444
Snippet 提出基于改进的在线多示例学习算法（Improved multiple instance learning,IMIL）的移动机器人目标跟踪方法.该方法利用射频识别系统（Radio frequency...
SourceID chongqing
SourceType Publisher
StartPage 2916
SubjectTerms 压缩特征
射频识别系统
改进的在线多示例学习
目标跟踪
Title 基于改进在线多示例学习算法的机器人目标跟踪
URI http://lib.cqvip.com/qk/90250X/201412/663229975.html
Volume 40
hasFullText 1
inHoldings 1
isFullTextHit
isPrint
link http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnVzNbtNAEF5F4QIHxK-AAqoQe4qCknht75zQOnGoEHAqqLfKdhx6SgtKLz0jcUDiBEK0ldpDhSokeuEAtQR9mTjOYzCz6yQrhCrgYo3Hu99MdjazM2vvMHa3IRMJ9EeKQaR1XAKSeiTjZh2ifgMSiHCK0QHnx0-8pafi4Yq7Uqn8sL5a2hzG95KtP54r-R-rIg_tSqdk_8GyM1BkII32xStaGK9_ZWMeuhy6PFA8FHSVIQ89DkgDDyUPuhwC3abNleShT22QiRwlOCjiKDHtHnKpG6sOV57mAFcN3Sbk4BNy4HBwiYN9pdCy2ro7igAtQqMFGhlFK60Pgkhf6-OTtkRIrpQdFhMT2ygNJZWGQqLNwbNU8vQjdzpHSIg0WguCDDqa6KCaNS1F_yhSsjMlELNbK1k0WCjSp289iAOod83CRIIw7W0RcxC19JuY89YpLzVLnPHr0he44pSbK6XjN3WiphO8ZbtxaHpWSNACczj7t8rcGKy1cDX3qcQBFSmqsjMPgkfP1DwcJYmW_3QBXaQVbnkulQOc3_v0Utt6C433jjNP7zB5pGqPs3uq_e9Z6bzbdDD5pHTaRCIuVWbSe4zlgFD9kLX1wfMXGBVZUdTyBXa-TH8WlZnLF1lla-0SO2cVxbzM7ud72Sh7O353PDnZyXcPi-wkP9guDrLRzzf5l0-j4_3i6MP46_ti-9V4N8s_Ho6yrNg5Gu-_nnzbm3z_fIXd6YbL7aX6TInVDVNSZXU2lM5VVh2sD9JrbFGIyOv5zbQve7EA9DSR0_MhkWmvnzQgcq-zhVOAbpz6dIGdpUlj9tJusurw5WZ6C6PLYXy7NOIv765g0Q
link.rule.ids 315,786,790,4043
linkProvider Elsevier
openUrl ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=%E5%9F%BA%E4%BA%8E%E6%94%B9%E8%BF%9B%E5%9C%A8%E7%BA%BF%E5%A4%9A%E7%A4%BA%E4%BE%8B%E5%AD%A6%E4%B9%A0%E7%AE%97%E6%B3%95%E7%9A%84%E6%9C%BA%E5%99%A8%E4%BA%BA%E7%9B%AE%E6%A0%87%E8%B7%9F%E8%B8%AA&rft.jtitle=%E8%87%AA%E5%8A%A8%E5%8C%96%E5%AD%A6%E6%8A%A5&rft.au=%E7%8E%8B%E4%B8%BD%E4%BD%B3+%E8%B4%BE%E6%9D%BE%E6%95%8F+%E6%9D%8E%E7%A7%80%E6%99%BA+%E7%8E%8B%E7%88%BD&rft.date=2014&rft.issn=0254-4156&rft.eissn=1874-1029&rft.volume=40&rft.issue=12&rft.spage=2916&rft.epage=2925&rft.externalDocID=663229975
thumbnail_s http://utb.summon.serialssolutions.com/2.0.0/image/custom?url=http%3A%2F%2Fimage.cqvip.com%2Fvip1000%2Fqk%2F90250X%2F90250X.jpg