Method for selecting representative videos for change detection datasets

In evaluating the change detection algorithms, the algorithm evaluated must show a superior performance than the state-of-the-art algorithms. The evaluation process steps comprise executing a new algorithm to segment a set of videos from a dataset and compare the results regarding a ground truth. In...

Full description

Saved in:
Bibliographic Details
Published inMultimedia tools and applications Vol. 81; no. 3; pp. 3773 - 3791
Main Authors Silva, Claudinei M., Rosa, Katharina A. I., Bugatti, Pedro H., Saito, Priscila T. M., Corrêa, Cléber G., Yokoyama, Roberto S., Sanches, Silvio R. R.
Format Journal Article
LanguageEnglish
Published New York Springer US 2022
Springer Nature B.V
Subjects
Online AccessGet full text

Cover

Loading…
Abstract In evaluating the change detection algorithms, the algorithm evaluated must show a superior performance than the state-of-the-art algorithms. The evaluation process steps comprise executing a new algorithm to segment a set of videos from a dataset and compare the results regarding a ground truth. In this paper, we propose using additional information in evaluating change detection algorithms: the level of difficulty in classifying a pixel. First, for each video frame used in the evaluation, we created a difficulty map structure, which stores values representing the level of difficulty required by an algorithm to classify each pixel of that frame. Second, we developed a metric to estimate each dataset video’s difficulty based on our difficulty maps. Third, we applied the metric to selecting the more representative videos from the dataset based on their difficulty level. Finally, to demonstrate the method’s contribution, we evaluated it using all videos from the CDNet 2014 dataset. The results showed that a subset of videos selected by our method has the same potential as the original CDNet 2014 dataset. Hence, a new change detection algorithm can be evaluated more quickly using our subset of videos selected.
AbstractList In evaluating the change detection algorithms, the algorithm evaluated must show a superior performance than the state-of-the-art algorithms. The evaluation process steps comprise executing a new algorithm to segment a set of videos from a dataset and compare the results regarding a ground truth. In this paper, we propose using additional information in evaluating change detection algorithms: the level of difficulty in classifying a pixel. First, for each video frame used in the evaluation, we created a difficulty map structure, which stores values representing the level of difficulty required by an algorithm to classify each pixel of that frame. Second, we developed a metric to estimate each dataset video’s difficulty based on our difficulty maps. Third, we applied the metric to selecting the more representative videos from the dataset based on their difficulty level. Finally, to demonstrate the method’s contribution, we evaluated it using all videos from the CDNet 2014 dataset. The results showed that a subset of videos selected by our method has the same potential as the original CDNet 2014 dataset. Hence, a new change detection algorithm can be evaluated more quickly using our subset of videos selected.
Author Corrêa, Cléber G.
Sanches, Silvio R. R.
Saito, Priscila T. M.
Bugatti, Pedro H.
Yokoyama, Roberto S.
Silva, Claudinei M.
Rosa, Katharina A. I.
Author_xml – sequence: 1
  givenname: Claudinei M.
  surname: Silva
  fullname: Silva, Claudinei M.
  organization: Universidade Tecnológica Federal do Paraná
– sequence: 2
  givenname: Katharina A. I.
  surname: Rosa
  fullname: Rosa, Katharina A. I.
  organization: Universidade Tecnológica Federal do Paraná
– sequence: 3
  givenname: Pedro H.
  surname: Bugatti
  fullname: Bugatti, Pedro H.
  organization: Universidade Tecnológica Federal do Paraná
– sequence: 4
  givenname: Priscila T. M.
  surname: Saito
  fullname: Saito, Priscila T. M.
  organization: Universidade Tecnológica Federal do Paraná
– sequence: 5
  givenname: Cléber G.
  surname: Corrêa
  fullname: Corrêa, Cléber G.
  organization: Universidade Tecnológica Federal do Paraná
– sequence: 6
  givenname: Roberto S.
  surname: Yokoyama
  fullname: Yokoyama, Roberto S.
  organization: Universidade Federal do ABC
– sequence: 7
  givenname: Silvio R. R.
  orcidid: 0000-0003-3635-7477
  surname: Sanches
  fullname: Sanches, Silvio R. R.
  email: silviosanches@utfpr.edu.br
  organization: Universidade Tecnológica Federal do Paraná
BookMark eNp9kLFOwzAQhi0EEm3hBZgiMRt8ZydORlQBRSpigdlykkubqsTFdivx9rgNEhvT3fD9_-m-KTsf3ECM3YC4AyH0fQAQCrlA4ACFEhzP2ARyLbnWCOdpl6XgOhdwyaYhbISAIkc1YYtXimvXZp3zWaAtNbEfVpmnnadAQ7SxP1B26Fty4cQ0azusKGspHlE3ZK2NNlAMV-yis9tA179zxj6eHt_nC758e36ZPyx5I6GKvNBtU1kp6grrGkBXbSmULUQpqGjrMkfCUjUIjcJKaSJSiHlRqgoRcqk7OWO3Y-_Ou689hWg2bu-HdNJgIdObEpRIFI5U410Injqz8_2n9d8GhDkaM6Mxk4yZkzGDKSTHUEhw-tL_Vf-T-gFUZG7j
CitedBy_id crossref_primary_10_1007_s11042_024_18271_3
Cites_doi 10.1007/s00521-009-0285-8
10.1117/1.3456695
10.1109/ACCESS.2019.2914961
10.1109/ICIP.2017.8297144
10.1186/s41074-017-0036-1
10.1109/TCSVT.2017.2711659
10.1109/ACCESS.2020.2997962
10.1016/j.neucom.2019.04.088
10.1109/ICCV.1999.791228
10.1016/j.patrec.2018.08.002
10.1109/CVPRW.2012.6238922
10.1109/ACCESS.2018.2812880
10.1007/978-3-319-58838-4_6
10.1109/ICME.2015.7177419
10.1109/ICPR.2004.1333992
10.1109/CVPRW.2012.6238919
10.3390/sym11050621
10.1016/j.patrec.2016.09.014
10.1109/CVPR.1999.784637
10.1016/j.patcog.2017.09.040
10.1109/CVPRW.2014.65
10.1016/j.neucom.2015.04.118
10.1109/CVPRW.2014.64
10.1007/s10044-019-00845-9
10.1007/978-3-319-68560-1_9
10.1109/CVPRW.2014.66
10.1109/WACV.2015.137
10.1109/IWSSIP.2015.7314229
10.1007/s10489-018-1346-4
10.1016/j.cviu.2013.12.005
10.1007/978-3-642-37410-4_25
10.1109/TIP.2017.2695882
10.1109/CVPRW.2014.68
10.1109/AVSS.2013.6636617
10.1109/TEVC.2017.2694160
10.1007/3-540-45053-X_48
10.1016/j.patcog.2014.10.020
10.1117/1.JEI.27.2.023002
10.1109/TIP.2014.2378053
10.1007/978-3-319-25903-1_12
10.1117/1.JEI.28.1.013038
10.1109/ICIP.2014.7025661
10.1007/s11042-020-09838-x
10.1109/VSPETS.2005.1570931
10.1109/ECTICon.2016.7561253
10.1007/978-3-319-29971-6_23
ContentType Journal Article
Copyright The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021
The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021.
Copyright_xml – notice: The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021
– notice: The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021.
DBID AAYXX
CITATION
3V.
7SC
7WY
7WZ
7XB
87Z
8AL
8AO
8FD
8FE
8FG
8FK
8FL
8G5
ABUWG
AFKRA
ARAPS
AZQEC
BENPR
BEZIV
BGLVJ
CCPQU
DWQXO
FRNLG
F~G
GNUQQ
GUQSH
HCIFZ
JQ2
K60
K6~
K7-
L.-
L7M
L~C
L~D
M0C
M0N
M2O
MBDVC
P5Z
P62
PQBIZ
PQBZA
PQEST
PQQKQ
PQUKI
Q9U
DOI 10.1007/s11042-021-11640-2
DatabaseName CrossRef
ProQuest Central (Corporate)
Computer and Information Systems Abstracts
ABI/INFORM Collection
ABI/INFORM Global (PDF only)
ProQuest Central (purchase pre-March 2016)
ABI/INFORM Global (Alumni Edition)
Computing Database (Alumni Edition)
ProQuest Pharma Collection
Technology Research Database
ProQuest SciTech Collection
ProQuest Technology Collection
ProQuest Central (Alumni) (purchase pre-March 2016)
ABI/INFORM Collection (Alumni Edition)
Research Library (Alumni Edition)
ProQuest Central (Alumni)
ProQuest Central
Advanced Technologies & Aerospace Collection
ProQuest Central Essentials
ProQuest Central
Business Premium Collection
Technology Collection
ProQuest One Community College
ProQuest Central Korea
Business Premium Collection (Alumni)
ABI/INFORM Global (Corporate)
ProQuest Central Student
Research Library Prep
SciTech Premium Collection
ProQuest Computer Science Collection
ProQuest Business Collection (Alumni Edition)
ProQuest Business Collection
Computer Science Database
ABI/INFORM Professional Advanced
Advanced Technologies Database with Aerospace
Computer and Information Systems Abstracts – Academic
Computer and Information Systems Abstracts Professional
ABI/INFORM Global
Computing Database
ProQuest research library
Research Library (Corporate)
Advanced Technologies & Aerospace Database
ProQuest Advanced Technologies & Aerospace Collection
ProQuest One Business
ProQuest One Business (Alumni)
ProQuest One Academic Eastern Edition (DO NOT USE)
ProQuest One Academic
ProQuest One Academic UKI Edition
ProQuest Central Basic
DatabaseTitle CrossRef
ABI/INFORM Global (Corporate)
ProQuest Business Collection (Alumni Edition)
ProQuest One Business
Research Library Prep
Computer Science Database
ProQuest Central Student
Technology Collection
Technology Research Database
Computer and Information Systems Abstracts – Academic
ProQuest Advanced Technologies & Aerospace Collection
ProQuest Central Essentials
ProQuest Computer Science Collection
Computer and Information Systems Abstracts
ProQuest Central (Alumni Edition)
SciTech Premium Collection
ProQuest One Community College
Research Library (Alumni Edition)
ProQuest Pharma Collection
ABI/INFORM Complete
ProQuest Central
ABI/INFORM Professional Advanced
ProQuest Central Korea
ProQuest Research Library
Advanced Technologies Database with Aerospace
ABI/INFORM Complete (Alumni Edition)
Advanced Technologies & Aerospace Collection
Business Premium Collection
ABI/INFORM Global
ProQuest Computing
ABI/INFORM Global (Alumni Edition)
ProQuest Central Basic
ProQuest Computing (Alumni Edition)
ProQuest One Academic Eastern Edition
ProQuest Technology Collection
ProQuest SciTech Collection
ProQuest Business Collection
Computer and Information Systems Abstracts Professional
Advanced Technologies & Aerospace Database
ProQuest One Academic UKI Edition
ProQuest One Business (Alumni)
ProQuest One Academic
ProQuest Central (Alumni)
Business Premium Collection (Alumni)
DatabaseTitleList ABI/INFORM Global (Corporate)

Database_xml – sequence: 1
  dbid: 8FG
  name: ProQuest Technology Collection
  url: https://search.proquest.com/technologycollection1
  sourceTypes: Aggregation Database
DeliveryMethod fulltext_linktorsrc
Discipline Engineering
Computer Science
EISSN 1573-7721
EndPage 3791
ExternalDocumentID 10_1007_s11042_021_11640_2
GroupedDBID -4Z
-59
-5G
-BR
-EM
-Y2
-~C
.4S
.86
.DC
.VR
06D
0R~
0VY
123
1N0
1SB
2.D
203
28-
29M
2J2
2JN
2JY
2KG
2LR
2P1
2VQ
2~H
30V
3EH
3V.
4.4
406
408
409
40D
40E
5QI
5VS
67Z
6NX
7WY
8AO
8FE
8FG
8FL
8G5
8UJ
95-
95.
95~
96X
AAAVM
AABHQ
AABYN
AAFGU
AAHNG
AAIAL
AAJKR
AANZL
AAOBN
AAPBV
AARHV
AARTL
AATNV
AATVU
AAUYE
AAWCG
AAWWR
AAYFA
AAYIU
AAYQN
AAYTO
ABBBX
ABBXA
ABDZT
ABECU
ABFGW
ABFTV
ABHLI
ABHQN
ABJNI
ABJOX
ABKAS
ABKCH
ABKTR
ABMNI
ABMQK
ABNWP
ABQBU
ABSXP
ABTEG
ABTHY
ABTKH
ABTMW
ABULA
ABUWG
ABWNU
ABXPI
ACBMV
ACBRV
ACBXY
ACBYP
ACGFO
ACGFS
ACHSB
ACHXU
ACIGE
ACIPQ
ACKNC
ACMDZ
ACMLO
ACOKC
ACOMO
ACREN
ACSNA
ACTTH
ACVWB
ACWMK
ADGRI
ADHHG
ADHIR
ADIMF
ADINQ
ADKNI
ADKPE
ADMDM
ADOXG
ADRFC
ADTPH
ADURQ
ADYFF
ADYOE
ADZKW
AEBTG
AEEQQ
AEFIE
AEFTE
AEGAL
AEGNC
AEJHL
AEJRE
AEKMD
AENEX
AEOHA
AEPYU
AESKC
AESTI
AETLH
AEVLU
AEVTX
AEXYK
AEYWE
AFEXP
AFGCZ
AFKRA
AFLOW
AFNRJ
AFQWF
AFWTZ
AFYQB
AFZKB
AGAYW
AGDGC
AGGBP
AGGDS
AGJBK
AGMZJ
AGQMX
AGWIL
AGWZB
AGYKE
AHAVH
AHBYD
AHKAY
AHSBF
AHYZX
AIAKS
AIIXL
AILAN
AIMYW
AITGF
AJBLW
AJDOV
AJRNO
AJZVZ
AKQUC
ALMA_UNASSIGNED_HOLDINGS
ALWAN
AMKLP
AMTXH
AMXSW
AMYLF
AMYQR
AOCGG
ARAPS
ARCSS
ARMRJ
ASPBG
AVWKF
AXYYD
AYJHY
AZFZN
AZQEC
B-.
BA0
BBWZM
BDATZ
BENPR
BEZIV
BGLVJ
BGNMA
BPHCQ
CAG
CCPQU
COF
CS3
CSCUP
DDRTE
DL5
DNIVK
DPUIP
DU5
DWQXO
EBLON
EBS
EIOEI
EJD
ESBYG
FEDTE
FERAY
FFXSO
FIGPU
FINBP
FNLPD
FRNLG
FRRFC
FSGXE
FWDCC
GGCAI
GGRSB
GJIRD
GNUQQ
GNWQR
GQ6
GQ7
GQ8
GROUPED_ABI_INFORM_COMPLETE
GUQSH
GXS
HCIFZ
HF~
HG5
HG6
HMJXF
HQYDN
HRMNR
HVGLF
HZ~
I-F
I09
IHE
IJ-
IKXTQ
ITG
ITH
ITM
IWAJR
IXC
IXE
IZIGR
IZQ
I~X
I~Z
J-C
J0Z
JBSCW
JCJTX
JZLTJ
K60
K6V
K6~
K7-
KDC
KOV
KOW
LAK
LLZTM
M0C
M0N
M2O
M4Y
MA-
N2Q
N9A
NB0
NDZJH
NPVJJ
NQJWS
NU0
O9-
O93
O9G
O9I
O9J
OAM
OVD
P19
P2P
P62
P9O
PF0
PQBIZ
PQQKQ
PROAC
PT4
PT5
Q2X
QOK
QOS
R4E
R89
R9I
RHV
RNI
RNS
ROL
RPX
RSV
RZC
RZE
RZK
S16
S1Z
S26
S27
S28
S3B
SAP
SCJ
SCLPG
SCO
SDH
SDM
SHX
SISQX
SJYHP
SNE
SNPRN
SNX
SOHCF
SOJ
SPISZ
SRMVM
SSLCW
STPWE
SZN
T13
T16
TEORI
TH9
TSG
TSK
TSV
TUC
TUS
U2A
UG4
UNUBA
UOJIU
UTJUX
UZXMN
VC2
VFIZW
W23
W48
WK8
YLTOR
Z45
Z5O
Z7R
Z7S
Z7V
Z7W
Z7X
Z7Y
Z7Z
Z81
Z83
Z86
Z88
Z8M
Z8N
Z8P
Z8Q
Z8R
Z8S
Z8T
Z8U
Z8W
Z92
ZMTXR
~EX
AACDK
AAEOY
AAGNY
AAJBT
AASML
AAYXX
AAYZH
ABAKF
ACAOD
ACDTI
ACZOJ
AEFQL
AEMSY
AFBBN
AGQEE
AGRTI
AIGIU
CITATION
H13
PQBZA
7SC
7XB
8AL
8FD
8FK
JQ2
L.-
L7M
L~C
L~D
MBDVC
PQEST
PQUKI
Q9U
ID FETCH-LOGICAL-c319t-67dc9a30b92bb1179d804a6080e6db852e284c21c42947eee42256849221537f3
IEDL.DBID AGYKE
ISSN 1380-7501
IngestDate Thu Nov 14 06:12:08 EST 2024
Thu Nov 21 21:06:41 EST 2024
Sat Dec 16 12:08:41 EST 2023
IsPeerReviewed true
IsScholarly true
Issue 3
Keywords Algorithm evaluation
Change detection
Dataset
Language English
LinkModel DirectLink
MergedId FETCHMERGED-LOGICAL-c319t-67dc9a30b92bb1179d804a6080e6db852e284c21c42947eee42256849221537f3
ORCID 0000-0003-3635-7477
PQID 2631383140
PQPubID 54626
PageCount 19
ParticipantIDs proquest_journals_2631383140
crossref_primary_10_1007_s11042_021_11640_2
springer_journals_10_1007_s11042_021_11640_2
PublicationCentury 2000
PublicationDate 1-2022
2022-01-00
20220101
PublicationDateYYYYMMDD 2022-01-01
PublicationDate_xml – year: 2022
  text: 1-2022
PublicationDecade 2020
PublicationPlace New York
PublicationPlace_xml – name: New York
– name: Dordrecht
PublicationSubtitle An International Journal
PublicationTitle Multimedia tools and applications
PublicationTitleAbbrev Multimed Tools Appl
PublicationYear 2022
Publisher Springer US
Springer Nature B.V
Publisher_xml – name: Springer US
– name: Springer Nature B.V
References Maddalena L, Petrosino A (2012) The sobs algorithm: what are the limits? In: 2012 IEEE computer society conference on computer vision and pattern recognition workshops, pp 21–26. https://doi.org/10.1109/CVPRW.2012.6238922
Lu X (2014) A multiscale spatio-temporal background model for motion detection. In: 2014 IEEE International Conference on Image Processing (ICIP), pp 3268–3271. https://doi.org/10.1109/ICIP.2014.7025661
Chen Y, Wang J, Lu H (2015) Learning sharable models for robust background subtraction. In: 2015 IEEE International Conference on Multimedia and Expo (ICME), pp 1–6. https://doi.org/10.1109/ICME.2015.7177419
ZhengWWangKWangFYA novel background subtraction algorithm based on parallel vision and bayesian gansNeurocomputing201910.1016/j.neucom.2019.04.088
Allebosch G, Van Hamme D, Deboeverie F, Veelaert P, Philips W (2016) C-efic: color and edge based foreground background segmentation with interior classification. In: Braz J, Pettré J, Richard P, Kerren A, Linsen L, Battiato S, Imai F (eds) Computer vision, imaging and computer graphics theory and applications. Springer International Publishing, Cham, pp 433–454. https://doi.org/10.1007/978-3-319-29971-6_23
Russel J, Cohn R (2013) Interquartile range. Tbilisi State University
Miron A, Badii A (2015) Change detection based on graph cuts. In: 2015 International conference on systems, signals and image processing (IWSSIP), pp 273–276. https://doi.org/10.1109/IWSSIP.2015.7314229
Université de Sherbrooke (2019) ChangeDetection.NET – a video database for testing change detection algorithms. http://www.changedetection.net. Accessed 22 Jul 2018
Fisher R (2019) CAVIAR test case scenarios. http://groups.inf.ed.ac.uk/vision/CAVIAR Accessed 24 Sep 2019
JiangSLuXWesambe: a weight-sample-based method for background subtractionIEEE Transactions on Circuits and Systems for Video Technology20182892105211510.1109/TCSVT.2017.2711659
ChanYTDeep learning-based scene-awareness approach for intelligent change detection in videosJournal of Electronic Imaging201928111210.1117/1.JEI.28.1.013038
BiancoSCioccaGSchettiniRCombination of video change detection algorithms by genetic programmingIEEE Transactions on Evolutionary Computation201721691492810.1109/TEVC.2017.2694160
Sedky M, Moniri M, Chibelushi CC (2014) Spectral-360: a physics-based technique for change detection. In: 2014 IEEE conference on computer vision and pattern recognition workshops, pp 405–408. https://doi.org/10.1109/CVPRW.2014.65
Vacavant A, Chateau T, Wilhelm A, Lequiévre L (2013) A benchmark dataset for outdoor foreground/background extraction. Springer, Berlin, pp 291–300. https://doi.org/10.1007/978-3-642-37410-4_25
SanchesSRRSementilleACAguilarIAFreireVRecommendations for evaluating the performance of background subtraction algorithms for surveillance systemsMultimed Tools Applic20218034421445410.1007/s11042-020-09838-x
Toyama K, Krumm J, Brumitt B, Meyers B (1999) Wallflower: principles and practice of background maintenance. In: Proceedings of the seventh IEEE international conference on computer vision, vol 1, pp 255–261. https://doi.org/10.1109/ICCV.1999.791228
LimLAKelesHYForeground segmentation using convolutional neural networks for multiscale feature encodingPattern Recogn Lett201811225626210.1016/j.patrec.2018.08.002
SanchesSRROliveiraCSementilleACFreireVChallenging situations for background subtraction algorithmsApplied Intelligence20194951771178410.1007/s10489-018-1346-4
Varadarajan S, Miller P, Zhou H (2013) Spatial mixture of gaussians for dynamic background modelling. In: 2013 10th IEEE international conference on advanced video and signal based surveillance, pp 63–68. https://doi.org/10.1109/AVSS.2013.6636617
Bianco S, Ciocca G, Schettini R (2017b) How far can you get by combining change detection algorithms? In: Battiato S, Gallo G, Schettini R, Stanco F (eds) Image analysis and processing - ICIAP 2017. Springer International Publishing, Cham, pp 96–107. https://doi.org/10.1007/978-3-319-68560-1_9
Gregorio MD, Giordano M (2017) Wisardrp for change detection in video sequences. In: 25th European symposium on artificial neural networks, computational intelligence and machine learning (ESANN 2017), pp 453–458
St-CharlesPBilodeauGBergevinRSubsense: a universal change detection method with local adaptive sensitivityIEEE Transactions on Image Processing2015241359373330067410.1109/TIP.2014.23780531408.94896
Wang B, Dudek P (2014) A fast self-tuning background subtraction algorithm. In: 2014 IEEE conference on computer vision and pattern recognition workshops, pp 401–404. https://doi.org/10.1109/CVPRW.2014.64
MaddalenaLPetrosinoAA fuzzy spatial coherence-based approach to background/foreground separation for moving object detectionNeural Computing and Applications201019217918610.1007/s00521-009-0285-8
Wang R, Bunyak F, Seetharaman G, Palaniappan K (2014) Static and moving object detection using flux tensor with split gaussian models. In: 2014 IEEE conference on computer vision and pattern recognition workshops, pp 420–424. https://doi.org/10.1109/CVPRW.2014.68
Wang Y, Luo Z, Jodoin PM (2017) Interactive deep learning method for segmenting moving objects. Pattern Recognition Letters 96:66–75 https://doi.org/10.1016/j.patrec.2016.09.014
LimLAKelesHYLearning multi-scale features for foreground segmentationPattern Analysis and Applications201910.1007/s10044-019-00845-9
Soomro K, Shah M (2012) Ucf101: a dataset of 101 human action classes from videos in the wild. Tech. rep., CRCV-TR-12-01
University of Naples Parthenope (2019) SceneBackgroundModeling.net.NET – a video database for testing background estimation algorithms. http://scenebackgroundmodeling.net. Accessed 24 Jul 2019
Braham M, Pierard S, Droogenbroeck MV (2017) Semantic background subtraction. In: 2017 IEEE International Conference on Image Processing (ICIP), pp 4552–4556
LeeShGcLeeYooJKwonSWisenetmd: motion detection using dynamic background region analysisSymmetry201911511510.3390/sym11050621
Young DP, Ferryman JM (2005) Pets metrics: on-line performance evaluation service. In: 2005 IEEE international workshop on visual surveillance and performance evaluation of tracking and surveillance, pp 317–324. https://doi.org/10.1109/VSPETS.2005.1570931
Elgammal A, Harwood D, Davis L (2000) Non-parametric model for background subtraction. In: Vernon D (ed) Computer vision — ECCV 2000. Springer, Berlin, pp 751–767.https://doi.org/10.1007/3-540-45053-X_48
Ramírez-AlonsoGChacon-MurguiaMIAuto-adaptive parallel som architecture with a modular analysis for dynamic object segmentation in videosNeurocomputing2016175990100010.1016/j.neucom.2015.04.118
YilmazAAGuzelMSBostanciEAskerzadeIA novel action recognition framework based on deep-learning and genetic algorithmsIEEE Access2020810063110064410.1109/ACCESS.2020.2997962
IsikSÖzkanKGünalSGerekONSwcd: a sliding window and self-regulated learning-based background updating method for change detection in videosJournal of Electronic Imaging201827211110.1117/1.JEI.27.2.023002
Microsoft Corporation (2019) Test images for wallflower paper. https://www.microsoft.com/en-us/download/details.aspx?id=54651. Accessed 9 Aug 2019
Stauffer C, Grimson WEL (1999) Adaptive background mixture models for real-time tracking. In: Proceedings. 1999 IEEE computer society conference on computer vision and pattern recognition (Cat. No PR00149), vol 2, 246–252. https://doi.org/10.1109/CVPR.1999.784637
BabaeeMDinhDTRigollGA deep convolutional neural network for video sequence background subtractionPattern Recogn20187663564910.1016/j.patcog.2017.09.040
SobralAVacavantAA comprehensive review of background subtraction algorithms evaluated with synthetic and real videosComputer Vision and Image Understanding201412242110.1016/j.cviu.2013.12.005
OpenCV team (2019) OpenCV. https://opencv.org/. Accessed 24 Sep 2019
Krungkaew R, Kusakunniran W (2016) Foreground segmentation in a video by using a novel dynamic codebook. 2016 13th International Conference on Electrical Engineering/Electronics. Computer, Telecommunications and Information Technology (ECTI-CON), pp 1–6
Martins I, Carvalho P, Corte-Real L, Alba-Castro JL (2017) Bmog: boosted gaussian mixture model with controlled complexity. In: Alexandre LA, Salvador Sánchez J, Rodrigues JMF (eds) Pattern recognition and image analysis. Springer International Publishing, Cham, pp 50–57. https://doi.org/10.1007/978-3-319-58838-4_6
SajidHCheungSSUniversal multimode background subtractionIEEE Transactions on Image Processing201726732493260365330910.1109/TIP.2017.26958821409.94523
Wang K, Gou C, Wang FY (2018) M4cd: A robust change detection method for intelligent visual surveillance. arXiv:1802.04979. Cornell University. Accessed 12 Nov 2019
BenezethYJodoinPMEmileBLaurentHRosenbergerCComparative study of background subtraction algorithmsJournal of Electronic Imaging201019311210.1117/1.3456695
KalsotraRAroraSA comprehensive survey of video datasets for background subtractionIEEE Access20197591435917110.1109/ACCESS.2019.2914961
St-Charles P, Bilodeau G, Bergevin R (2015a) A self-adjusting approach to change detection based on background word consensus. In: 2015 IEEE winter conference on applications of computer vision, pp 990–997 https://doi.org/10.1109/WACV.2015.137
ZhengWWangKWangFYA novel background subtraction algorithm based on parallel vision and bayesian gansNeurocomputing202039417820010.1016/j.neucom.2019.04.088
VargheseAGSSample-based integrated background subtraction and shadow detectionIPSJ Transactions on Computer Vision and Applications2017912510.1186/s41074-017-0036-1
LiangDKanekoSHashimotoMIwataKZhaoXCo-occurrence probability-based pixel pairs background model for robust object detection in dynamic scenesPattern Recogn20154841374139010.1016/j.patcog.2014.10.020
Goyette N, Jodoin PM, Porikli F, Konrad J, Ishwar P (2012) Changedetection.net: a new change detection benchmark dataset. In: 2012 IEEE computer society conference on computer vision and pattern recognition workshops, pp 1–8. https://doi.org/10.1109/CVPRW.2012.6238919
Gregorio M
AGS Varghese (11640_CR46) 2017; 9
11640_CR37
11640_CR38
R Kalsotra (11640_CR17) 2019; 7
11640_CR47
11640_CR44
11640_CR45
11640_CR42
11640_CR43
11640_CR40
11640_CR41
Y Benezeth (11640_CR4) 2010; 19
YT Chan (11640_CR8) 2019; 28
L Maddalena (11640_CR24) 2010; 19
A Sobral (11640_CR36) 2014; 122
11640_CR28
S Bianco (11640_CR5) 2017; 21
11640_CR29
G Ramírez-Alonso (11640_CR30) 2016; 175
cr-split#-11640_CR10.2
11640_CR26
H Sajid (11640_CR32) 2017; 26
cr-split#-11640_CR10.1
11640_CR27
11640_CR35
S Jiang (11640_CR16) 2018; 28
11640_CR9
11640_CR7
11640_CR31
11640_CR6
11640_CR2
11640_CR1
AA Yilmaz (11640_CR51) 2020; 8
LA Lim (11640_CR22) 2019
11640_CR18
M Babaee (11640_CR3) 2018; 76
11640_CR25
W Zheng (11640_CR54) 2020; 394
S Isik (11640_CR15) 2018; 27
11640_CR23
LA Lim (11640_CR21) 2018; 112
W Zheng (11640_CR53) 2019
SRR Sanches (11640_CR33) 2019; 49
Sh Lee (11640_CR19) 2019; 11
D Liang (11640_CR20) 2015; 48
11640_CR48
11640_CR49
11640_CR13
11640_CR14
11640_CR11
11640_CR55
11640_CR12
P St-Charles (11640_CR39) 2015; 24
11640_CR52
11640_CR50
SRR Sanches (11640_CR34) 2021; 80
References_xml – volume: 19
  start-page: 179
  issue: 2
  year: 2010
  ident: 11640_CR24
  publication-title: Neural Computing and Applications
  doi: 10.1007/s00521-009-0285-8
  contributor:
    fullname: L Maddalena
– ident: 11640_CR29
– volume: 19
  start-page: 1
  issue: 3
  year: 2010
  ident: 11640_CR4
  publication-title: Journal of Electronic Imaging
  doi: 10.1117/1.3456695
  contributor:
    fullname: Y Benezeth
– ident: 11640_CR31
– volume: 7
  start-page: 59143
  year: 2019
  ident: 11640_CR17
  publication-title: IEEE Access
  doi: 10.1109/ACCESS.2019.2914961
  contributor:
    fullname: R Kalsotra
– ident: 11640_CR7
  doi: 10.1109/ICIP.2017.8297144
– volume: 9
  start-page: 25
  issue: 1
  year: 2017
  ident: 11640_CR46
  publication-title: IPSJ Transactions on Computer Vision and Applications
  doi: 10.1186/s41074-017-0036-1
  contributor:
    fullname: AGS Varghese
– volume: 28
  start-page: 2105
  issue: 9
  year: 2018
  ident: 11640_CR16
  publication-title: IEEE Transactions on Circuits and Systems for Video Technology
  doi: 10.1109/TCSVT.2017.2711659
  contributor:
    fullname: S Jiang
– volume: 8
  start-page: 100631
  year: 2020
  ident: 11640_CR51
  publication-title: IEEE Access
  doi: 10.1109/ACCESS.2020.2997962
  contributor:
    fullname: AA Yilmaz
– volume: 394
  start-page: 178
  year: 2020
  ident: 11640_CR54
  publication-title: Neurocomputing
  doi: 10.1016/j.neucom.2019.04.088
  contributor:
    fullname: W Zheng
– ident: 11640_CR41
  doi: 10.1109/ICCV.1999.791228
– volume: 112
  start-page: 256
  year: 2018
  ident: 11640_CR21
  publication-title: Pattern Recogn Lett
  doi: 10.1016/j.patrec.2018.08.002
  contributor:
    fullname: LA Lim
– ident: 11640_CR25
  doi: 10.1109/CVPRW.2012.6238922
– ident: 11640_CR48
  doi: 10.1109/ACCESS.2018.2812880
– ident: 11640_CR26
  doi: 10.1007/978-3-319-58838-4_6
– ident: 11640_CR9
  doi: 10.1109/ICME.2015.7177419
– ident: 11640_CR55
  doi: 10.1109/ICPR.2004.1333992
– ident: 11640_CR12
  doi: 10.1109/CVPRW.2012.6238919
– volume: 11
  start-page: 1
  issue: 5
  year: 2019
  ident: 11640_CR19
  publication-title: Symmetry
  doi: 10.3390/sym11050621
  contributor:
    fullname: Sh Lee
– ident: 11640_CR50
  doi: 10.1016/j.patrec.2016.09.014
– ident: 11640_CR40
  doi: 10.1109/CVPR.1999.784637
– volume: 76
  start-page: 635
  year: 2018
  ident: 11640_CR3
  publication-title: Pattern Recogn
  doi: 10.1016/j.patcog.2017.09.040
  contributor:
    fullname: M Babaee
– ident: 11640_CR35
  doi: 10.1109/CVPRW.2014.65
– ident: 11640_CR27
– volume: 175
  start-page: 990
  year: 2016
  ident: 11640_CR30
  publication-title: Neurocomputing
  doi: 10.1016/j.neucom.2015.04.118
  contributor:
    fullname: G Ramírez-Alonso
– ident: 11640_CR47
  doi: 10.1109/CVPRW.2014.64
– year: 2019
  ident: 11640_CR22
  publication-title: Pattern Analysis and Applications
  doi: 10.1007/s10044-019-00845-9
  contributor:
    fullname: LA Lim
– ident: 11640_CR6
  doi: 10.1007/978-3-319-68560-1_9
– ident: 11640_CR13
  doi: 10.1109/CVPRW.2014.66
– ident: 11640_CR42
– ident: 11640_CR14
– ident: 11640_CR38
  doi: 10.1109/WACV.2015.137
– ident: 11640_CR28
  doi: 10.1109/IWSSIP.2015.7314229
– ident: 11640_CR37
– volume: 49
  start-page: 1771
  issue: 5
  year: 2019
  ident: 11640_CR33
  publication-title: Applied Intelligence
  doi: 10.1007/s10489-018-1346-4
  contributor:
    fullname: SRR Sanches
– volume: 122
  start-page: 4
  year: 2014
  ident: 11640_CR36
  publication-title: Computer Vision and Image Understanding
  doi: 10.1016/j.cviu.2013.12.005
  contributor:
    fullname: A Sobral
– ident: 11640_CR44
  doi: 10.1007/978-3-642-37410-4_25
– volume: 26
  start-page: 3249
  issue: 7
  year: 2017
  ident: 11640_CR32
  publication-title: IEEE Transactions on Image Processing
  doi: 10.1109/TIP.2017.2695882
  contributor:
    fullname: H Sajid
– ident: 11640_CR11
– ident: 11640_CR49
  doi: 10.1109/CVPRW.2014.68
– ident: 11640_CR45
  doi: 10.1109/AVSS.2013.6636617
– volume: 21
  start-page: 914
  issue: 6
  year: 2017
  ident: 11640_CR5
  publication-title: IEEE Transactions on Evolutionary Computation
  doi: 10.1109/TEVC.2017.2694160
  contributor:
    fullname: S Bianco
– ident: #cr-split#-11640_CR10.2
  doi: 10.1007/3-540-45053-X_48
– volume: 48
  start-page: 1374
  issue: 4
  year: 2015
  ident: 11640_CR20
  publication-title: Pattern Recogn
  doi: 10.1016/j.patcog.2014.10.020
  contributor:
    fullname: D Liang
– year: 2019
  ident: 11640_CR53
  publication-title: Neurocomputing
  doi: 10.1016/j.neucom.2019.04.088
  contributor:
    fullname: W Zheng
– ident: 11640_CR43
– volume: 27
  start-page: 1
  issue: 2
  year: 2018
  ident: 11640_CR15
  publication-title: Journal of Electronic Imaging
  doi: 10.1117/1.JEI.27.2.023002
  contributor:
    fullname: S Isik
– volume: 24
  start-page: 359
  issue: 1
  year: 2015
  ident: 11640_CR39
  publication-title: IEEE Transactions on Image Processing
  doi: 10.1109/TIP.2014.2378053
  contributor:
    fullname: P St-Charles
– ident: 11640_CR1
  doi: 10.1007/978-3-319-25903-1_12
– ident: #cr-split#-11640_CR10.1
  doi: 10.1007/3-540-45053-X_48
– volume: 28
  start-page: 1
  issue: 1
  year: 2019
  ident: 11640_CR8
  publication-title: Journal of Electronic Imaging
  doi: 10.1117/1.JEI.28.1.013038
  contributor:
    fullname: YT Chan
– ident: 11640_CR23
  doi: 10.1109/ICIP.2014.7025661
– volume: 80
  start-page: 4421
  issue: 3
  year: 2021
  ident: 11640_CR34
  publication-title: Multimed Tools Applic
  doi: 10.1007/s11042-020-09838-x
  contributor:
    fullname: SRR Sanches
– ident: 11640_CR52
  doi: 10.1109/VSPETS.2005.1570931
– ident: 11640_CR18
  doi: 10.1109/ECTICon.2016.7561253
– ident: 11640_CR2
  doi: 10.1007/978-3-319-29971-6_23
SSID ssj0016524
Score 2.332495
Snippet In evaluating the change detection algorithms, the algorithm evaluated must show a superior performance than the state-of-the-art algorithms. The evaluation...
SourceID proquest
crossref
springer
SourceType Aggregation Database
Publisher
StartPage 3773
SubjectTerms Algorithms
Change detection
Classification
Computer Communication Networks
Computer Science
Data Structures and Information Theory
Datasets
Multimedia Information Systems
Pixels
Special Purpose and Application-Based Systems
State-of-the-art reviews
Video data
SummonAdditionalLinks – databaseName: ProQuest Technology Collection
  dbid: 8FG
  link: http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwfV09T8MwED1BWWDgo4AoFOSBDSwSx3GSCSFEqZDKRKVukRNfx7SQwO_n7DgEkGBNIg_nnN_d-d49gMtCxrEhJOURoqAERWueakpck0guCS61iUrLd549q-lcPi3ihS-41b6tsjsT3UFtVqWtkd8IFYWUTVE-cLt-5VY1yt6uegmNTdgKRaJsS186efy6RVCxF7VNA07IGHrSTEudCy0xxTYohJQxBFz8BKY-2vx1QepwZ7IPuz5gZHftDh_ABlZD2OvEGJj3zSHsfJsseAjTmVOGZhSSstpJ3dBj5iZYerbRBzJLwVvV7puW_8sMNq41q2K2c7TGpj6C-eTh5X7KvWgCL8mbGq4SU2Y6CopMFIWd92bSQGpFgSEqU6SxQAKkUoQlAZFMEFGSR6tUZoLAP0qW0TEMqlWFJ8C0NoiBTKQlw1EokGFRLlNVIIYmCeRyBFedxfJ1Oxsj76cgW_vmZN_c2TcXIxh3Rs29n9R5v6sjuO4M3b_-e7XT_1c7g21heQquVjKGQfP2jucUPTTFhftFPgE8wb7O
  priority: 102
  providerName: ProQuest
Title Method for selecting representative videos for change detection datasets
URI https://link.springer.com/article/10.1007/s11042-021-11640-2
https://www.proquest.com/docview/2631383140
Volume 81
hasFullText 1
inHoldings 1
isFullTextHit
isPrint
link http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV09T8MwED1Bu8BAoYAolMoDG6RKHOejY4uaVqBWCLVSmaI4uS5ILSIpA7-es5MQPodOkWLLUs4-v-f43h3AlRSOkxCSGjYipwNKFBl-RAdXzxZLgssosWOld55M3fFc3C2cRaXj1sHu5Y2k3qgrrZullCQqosAiim8atO_WCXuUN9b7o6f74eflgesUtWx90yBAtAqtzN-jfMejimT-uBfVcBM0YFaKdvIok-fuJpPd-P13DsdtvuQQDgr6yfr5ejmCHVw1oVGWdmCFpzdh_0uewmMYT3SdaUYEl6W6cA69ZjofZqFdekOmBH3rVPfJ1cQswUwHeq2YikNNMUtPYB4MZ7djoyjBYMTkm5nheknci2xT9riUKntc4psicolmoptI3-FI8BZzKyZYEx4iCtofXF_0OFEJ21vap1BbrVd4BiyKEkRTeEJJ64hY9FDGS9-ViFbimWLZgutyIsKXPNNGWOVUViYLyWShNlnIW9Au5yosvC4NuWvTpNt0ZmzBTWn7qvn_0c63634Be1ypIPSfmDbUstcNXhI3yWQHdv1g1KEVGQwG006xMuk5GE4fHql1zvsf-eHcgQ
link.rule.ids 314,780,784,12765,21388,27924,27925,33373,33744,41081,41523,42150,42592,43600,43805,52111,52234,74035,74302
linkProvider Springer Nature
linkToHtml http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwfV09T8MwED1BGYCBjwKiUMADG0QkjvM1IYQoAdpOrdQtSuLLmBYS-P2cHYcAEqxJ5OGc87s737sHcJkJz5OEpJaLyClBSVMrTClxDVxREFym0s0V33ky9eO5eF54C1Nwq0xbZXsm6oNaLnNVI7_hvutQNkX5wO3q1VKqUep21UhorMOGcAm6FVN89Ph1i-B7RtQ2tC1CRseQZhrqnKOIKapBwaGMwbb4T2Dqos1fF6Qad0Z7sGMCRnbX7PA-rGHZh91WjIEZ3-zD9rfJggcQT7QyNKOQlFVa6oYeMz3B0rCNPpApCt6y0t80_F8msdatWSVTnaMV1tUhzEcPs_vYMqIJVk7eVFt-IPMode0s4lmm5r3J0BapT4Eh-jILPY4ESDl3cgIiESCiII_2QxFxAn83KNwj6JXLEo-BpalEtEUgFBmOQoEIs7wI_QzRkYEtigFctRZLVs1sjKSbgqzsm5B9E23fhA9g2Bo1MX5SJd2uDuC6NXT3-u_VTv5f7QI249lknIyfpi-nsMUVZ0HXTYbQq9_e8YwiiTo717_LJz-NwbA
linkToPdf http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwfV1LT8MwDLZgkxAceAwQgwE5cIOKNk1fJ8Rj03hsmhCTdqvaxj12gxZ-P06WUkCCa1Pl4MT57MSfP4CzVHieJCS1XEROCUqSWGFCiWvgipzgMpFupvjOo7E_nIqHmTcz9U-lKausz0R9UMt5pu7IL7nvOpRNUT5wmZuyiMnd4GrxaikFKfXSauQ0VqFNqGjzFrRv-uPJ89ebgu8ZidvQtggnHUOhWRLpHEVTUeUKDuUPtsV_wlQTe_56LtUoNNiGTRM-suvleu_AChYd2KqlGZjx1A5sfOszuAvDkdaJZhSgslIL39BnpvtZGu7RBzJFyJuX-p8lG5hJrHShVsFUHWmJVbkH00H_5XZoGQkFKyPfqiw_kFmUuHYa8TRV3d9kaIvEpzARfZmGHkeCp4w7GcGSCBBRkH_7oYg4hQJukLv70CrmBR4ASxKJaItAKGocBQYRplke-imiIwNb5F04ry0WL5adMuKmJ7Kyb0z2jbV9Y96FXm3U2HhNGTdr3IWL2tDN8N-zHf4_2yms0V6Jn-7Hj0ewzhWBQV-i9KBVvb3jMYUVVXpi9ssn03DHTA
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=Method+for+selecting+representative+videos+for+change+detection+datasets&rft.jtitle=Multimedia+tools+and+applications&rft.au=Silva%2C+Claudinei+M.&rft.au=Rosa%2C+Katharina+A.+I.&rft.au=Bugatti%2C+Pedro+H.&rft.au=Saito%2C+Priscila+T.+M.&rft.date=2022-01-01&rft.pub=Springer+US&rft.issn=1380-7501&rft.eissn=1573-7721&rft.volume=81&rft.issue=3&rft.spage=3773&rft.epage=3791&rft_id=info:doi/10.1007%2Fs11042-021-11640-2&rft.externalDocID=10_1007_s11042_021_11640_2
thumbnail_l http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1380-7501&client=summon
thumbnail_m http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1380-7501&client=summon
thumbnail_s http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1380-7501&client=summon