Features for Multi-target Multi-camera Tracking and Re-identification

Multi-Target Multi-Camera Tracking (MTMCT) tracks many people through video taken from several cameras. Person Re-Identification (Re-ID) retrieves from a gallery images of people similar to a person query image. We learn good features for both MTMCT and Re-ID with a convolutional neural network. Our...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition S. 6036 - 6046
Hauptverfasser: Ristani, Ergys, Tomasi, Carlo
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 01.06.2018
Schlagworte:
Benchmark testing
Cameras
Correlation
Detectors
Feature extraction
Training
Trajectory
ISSN:1063-6919
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Abstract Multi-Target Multi-Camera Tracking (MTMCT) tracks many people through video taken from several cameras. Person Re-Identification (Re-ID) retrieves from a gallery images of people similar to a person query image. We learn good features for both MTMCT and Re-ID with a convolutional neural network. Our contributions include an adaptive weighted triplet loss for training and a new technique for hard-identity mining. Our method outperforms the state of the art both on the DukeMTMC benchmarks for tracking, and on the Market-1501 and DukeMTMC-ReID benchmarks for Re-ID. We examine the correlation between good Re-ID and good MTMCT scores, and perform ablation studies to elucidate the contributions of the main components of our system. Code is available1.
AbstractList Multi-Target Multi-Camera Tracking (MTMCT) tracks many people through video taken from several cameras. Person Re-Identification (Re-ID) retrieves from a gallery images of people similar to a person query image. We learn good features for both MTMCT and Re-ID with a convolutional neural network. Our contributions include an adaptive weighted triplet loss for training and a new technique for hard-identity mining. Our method outperforms the state of the art both on the DukeMTMC benchmarks for tracking, and on the Market-1501 and DukeMTMC-ReID benchmarks for Re-ID. We examine the correlation between good Re-ID and good MTMCT scores, and perform ablation studies to elucidate the contributions of the main components of our system. Code is available1.
Author Ristani, Ergys
Tomasi, Carlo
Author_xml – sequence: 1
  givenname: Ergys
  surname: Ristani
  fullname: Ristani, Ergys
– sequence: 2
  givenname: Carlo
  surname: Tomasi
  fullname: Tomasi, Carlo
BookMark eNotjz1PwzAURQ0CiVIyM7DkDyT449mxRxS1BakIVBXWyk6eK0PrIMcd-PdEosPV1bnDke4tuYpDRELuGa0Zo-ax_Xzf1JwyXVOqBL8ghWk0k0IrBZyaSzJj014pw8wNKcbxi1LKlRYa5IwslmjzKeFY-iGVr6dDDlW2aY_5DJ09YrLlNtnuO8R9aWNfbrAKPcYcfOhsDkO8I9feHkYszj0nH8vFtn2u1m-rl_ZpXQXWyFwBGJAdA4XgOXjDO6aMExQ89FR652D6Y52f0nPrWI-uAy6dbsD0yngxJw__3oCIu58Ujjb97rRsdCOo-AOsXU2g
CODEN IEEPAD
ContentType Conference Proceeding
DBID 6IE
6IH
CBEJK
RIE
RIO
DOI 10.1109/CVPR.2018.00632
DatabaseName IEEE Electronic Library (IEL) Conference Proceedings
IEEE Proceedings Order Plan (POP) 1998-present by volume
IEEE Xplore All Conference Proceedings
IEEE Electronic Library (IEL)
IEEE Proceedings Order Plans (POP) 1998-present
DatabaseTitleList
Database_xml – sequence: 1
  dbid: RIE
  name: IEEE Electronic Library (IEL)
  url: https://ieeexplore.ieee.org/
  sourceTypes: Publisher
DeliveryMethod fulltext_linktorsrc
Discipline Applied Sciences
EISBN 9781538664209
1538664208
EISSN 1063-6919
EndPage 6046
ExternalDocumentID 8578730
Genre orig-research
GroupedDBID 6IE
6IH
6IL
6IN
AAWTH
ABLEC
ADZIZ
ALMA_UNASSIGNED_HOLDINGS
BEFXN
BFFAM
BGNUA
BKEBE
BPEOZ
CBEJK
CHZPO
IEGSK
IJVOP
OCL
RIE
RIL
RIO
ID FETCH-LOGICAL-i175t-44945c146e4f24f92c169b304f4d05fbb4109abf9abd2ab1debc425b8749d69f3
IEDL.DBID RIE
ISICitedReferencesCount 409
ISICitedReferencesURI http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000457843606020&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
IngestDate Wed Aug 27 02:52:16 EDT 2025
IsPeerReviewed false
IsScholarly true
Language English
LinkModel DirectLink
MergedId FETCHMERGED-LOGICAL-i175t-44945c146e4f24f92c169b304f4d05fbb4109abf9abd2ab1debc425b8749d69f3
PageCount 11
ParticipantIDs ieee_primary_8578730
PublicationCentury 2000
PublicationDate 2018-Jun
PublicationDateYYYYMMDD 2018-06-01
PublicationDate_xml – month: 06
  year: 2018
  text: 2018-Jun
PublicationDecade 2010
PublicationTitle 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
PublicationTitleAbbrev CVPR
PublicationYear 2018
Publisher IEEE
Publisher_xml – name: IEEE
SSID ssj0002683845
ssj0003211698
Score 2.61627
Snippet Multi-Target Multi-Camera Tracking (MTMCT) tracks many people through video taken from several cameras. Person Re-Identification (Re-ID) retrieves from a...
SourceID ieee
SourceType Publisher
StartPage 6036
SubjectTerms Benchmark testing
Cameras
Correlation
Detectors
Feature extraction
Training
Trajectory
Title Features for Multi-target Multi-camera Tracking and Re-identification
URI https://ieeexplore.ieee.org/document/8578730
WOSCitedRecordID wos000457843606020&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
hasFullText 1
inHoldings 1
isFullTextHit
isPrint
link http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV25TgMxEB2FiIKKI0HcckGJyR6OjzpKRIGiKAKULvIppdmgHHw_Hq8VKGgoVlpvtXo-5vB7MwCPWg8tKx2nMphAmRWcqlAJLISsGHc6yDIJhV_FdCoXCzXrwNNBC-O9T-Qz_4yv6S7fre0eU2UDicurjgH6kRCi1Wod8ikVl7XMN2Q4rmNkw5XM1XzKQg1GH7M5crmQPMmx3civdirJmkxO__cfZ9D_keWR2cHgnEPHNxdwmv1Iknfptgdj9Ov2MY4m0SMlSWJLW8Z3HliNqSgS7ZTFTDnRjSNzT1cuU4fSbPXhfTJ-G73Q3C6BrqIPsKOMKTa08eTzLFQsqMpGAExdsMBcMQzGsIiCNiE-rtKmdN7YuGONFEw5rkJ9Cd1m3fgrIKjWrUtvvWQeq9uYwkpTaFkGgweEuoYeorL8bCtiLDMgN39_voUThL0lWN1Bd7fZ-3s4tl-71XbzkKbxG6AknTw
linkProvider IEEE
linkToHtml http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1LTwIxEG4ImugJFYxve_BoZR-zu-2ZQDAiIQQNN9JnwmUhPPz9dnYb9ODFQ5N2T5uZtvPo980Q8iRlpiE2OeNOOQa6yJlwSYGFkAXkRjoeV0ThUTEe8_lcTBrk-cCFsdZW4DP7gtPqLd-s9B5TZV2O2yv1AfpRBpDENVvrkFFJcp7y8EaG69THNrngoZ5PHIlu73MyRTQXwidzbDjyq6FKZU8Grf_9yRnp_BDz6ORgcs5Jw5YXpBU8SRrO6bZN-ujZ7X0kTb1PSiuSLasx32GhJSajqLdUGnPlVJaGTi1bmgAeqvTVIR-D_qw3ZKFhAlt6L2DHAARk2t99FlwCTiTaC0ClETgwUeaUAi8FqZwfJpEqNlZpf2YVL0CYXLj0kjTLVWmvCEW-bhpbbTlYrG-jIs1VJHnsFF4R4pq0USqLdV0TYxEEcvP350dyMpy9jxaj1_HbLTlFFdRwqzvS3G329p4c66_dcrt5qFT6Df9JoIM
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%3Abook&rft.genre=proceeding&rft.title=2018+IEEE%2FCVF+Conference+on+Computer+Vision+and+Pattern+Recognition&rft.atitle=Features+for+Multi-target+Multi-camera+Tracking+and+Re-identification&rft.au=Ristani%2C+Ergys&rft.au=Tomasi%2C+Carlo&rft.date=2018-06-01&rft.pub=IEEE&rft.eissn=1063-6919&rft.spage=6036&rft.epage=6046&rft_id=info:doi/10.1109%2FCVPR.2018.00632&rft.externalDocID=8578730