Object detection and tracking under Complex environment using deep learning-based LPM
Object detection and tracking under complex environment are challenging because of the disturbances induced by background clutter, illumination changes, occlusions and other factors. The bulk of traditional algorithms basically rely on hand-crafted features, which are not sufficiently robust to a co...
Uloženo v:
| Vydáno v: | IET computer vision Ročník 13; číslo 2; s. 157 - 164 |
|---|---|
| Hlavní autoři: | , , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
The Institution of Engineering and Technology
01.03.2019
Wiley |
| Témata: | |
| ISSN: | 1751-9632, 1751-9640, 1751-9640 |
| On-line přístup: | Získat plný text |
| Tagy: |
Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
|
| Abstract | Object detection and tracking under complex environment are challenging because of the disturbances induced by background clutter, illumination changes, occlusions and other factors. The bulk of traditional algorithms basically rely on hand-crafted features, which are not sufficiently robust to a complex environment. Moreover, the processes of detection and tracking are separated, which leads to the overall efficiency not high. In this study, a novel local probability model (LPM)-based mean shift (MS) algorithm is proposed to integrate object detection and tracking. The main contributions include: (i) a new framework based on the combination of LPM and MS is established for the integration of object tracking and detection. (ii) For object detection, the training and prediction of LPM are built by stacked denoising autoencoders based deep learning. (iii) For object tracking, an MS tracking algorithm leveraging LPM is modified to improve the tracking efficiency under a complex environment. Experimental results demonstrate that the proposed method is superior to the colour histograms based MS and histograms of oriented gradients based MS in terms of robustness and tracking accuracy. |
|---|---|
| AbstractList | Object detection and tracking under complex environment are challenging because of the disturbances induced by background clutter, illumination changes, occlusions and other factors. The bulk of traditional algorithms basically rely on hand‐crafted features, which are not sufficiently robust to a complex environment. Moreover, the processes of detection and tracking are separated, which leads to the overall efficiency not high. In this study, a novel local probability model (LPM)‐based mean shift (MS) algorithm is proposed to integrate object detection and tracking. The main contributions include: (i) a new framework based on the combination of LPM and MS is established for the integration of object tracking and detection. (ii) For object detection, the training and prediction of LPM are built by stacked denoising autoencoders based deep learning. (iii) For object tracking, an MS tracking algorithm leveraging LPM is modified to improve the tracking efficiency under a complex environment. Experimental results demonstrate that the proposed method is superior to the colour histograms based MS and histograms of oriented gradients based MS in terms of robustness and tracking accuracy. |
| Author | Zhou, Qichen Cao, Xianbin Li, Yundong Zhang, Xueyan Li, Hongguang Xiao, Zhifeng |
| Author_xml | – sequence: 1 givenname: Yundong surname: Li fullname: Li, Yundong organization: 1School of Electronic and Information Engineering, North China University of Technology, Beijing, People's Republic of China – sequence: 2 givenname: Xueyan surname: Zhang fullname: Zhang, Xueyan organization: 1School of Electronic and Information Engineering, North China University of Technology, Beijing, People's Republic of China – sequence: 3 givenname: Hongguang surname: Li fullname: Li, Hongguang email: lihongguang@buaa.edu.cn organization: 2Unmanned Systems Research Institute, Beihang University, Beijing, People's Republic of China – sequence: 4 givenname: Qichen surname: Zhou fullname: Zhou, Qichen organization: 1School of Electronic and Information Engineering, North China University of Technology, Beijing, People's Republic of China – sequence: 5 givenname: Xianbin surname: Cao fullname: Cao, Xianbin organization: 3School of Electronic and Information Engineering, Beihang University, Beijing, People's Republic of China – sequence: 6 givenname: Zhifeng surname: Xiao fullname: Xiao, Zhifeng organization: 4State Key Laboratory of Information Engineering in Surveying, Wuhan University, Wuhan, People's Republic of China |
| BookMark | eNqFkctuGyEUhlGVSonTPkB3vMC43OZCd61VN5YcOVKbbBGXQ4Q7BosZO83bh4mjLLJIVwcOfP8RHzN0FlMEhL5QMqdEyK8Bxsoew5wR2s1ryuQHdEHbmlayEeTsdc3ZOZoNw5aQupFSXKDbjdmCHbGDsZSQItbR4TFr-zfEe3yIDjJepN2-h38Y4jHkFHcQR3wYpnMHsMc96BzLrjJ6AIfXN9ef0Eev-wE-v9RL9Hv588_iqlpvfq0W39eVFYSwinpBvRVetp0H6KimxAqja2gM16JrnOx402guhGGGtZL62kpWHme8p5xfotUp1SW9Vfscdjo_qqSDem6kfK90HoPtQRHjuNWeOyqtaFyniWvrzlEOtYEOTMmipyyb0zBk8K95lKjJsCqGVTGsJsNqMlyY9g1jw6gnicVf6N8lv53Ih9DD4_9HqcXdiv1Yll-rWYGrEzxd26ZDjkXxO8OeAKX5ptI |
| CitedBy_id | crossref_primary_10_1016_j_asoc_2023_110176 crossref_primary_10_1007_s00521_024_10853_4 crossref_primary_10_1007_s13369_021_06275_2 crossref_primary_10_1186_s12938_022_01001_x crossref_primary_10_1109_TIE_2020_3021640 crossref_primary_10_1109_TITS_2022_3156267 crossref_primary_10_1080_10447318_2024_2327197 |
| Cites_doi | 10.1109/CVPRW.2014.126 10.1109/TIP.2017.2718189 10.1007/s00371-014-1014-6 10.1109/ICCV.2007.4408954 10.1109/ICCV.1999.790416 10.1007/s11760-014-0612-0 10.1109/CVPR.2009.5206502 10.1109/TIP.2007.914150 10.1109/TASE.2016.2520955 10.1109/ICIP.2010.5653525 10.1016/j.patcog.2017.03.030 10.1145/1390156.1390294 10.1007/s00371-014-1044-0 10.1016/j.neucom.2013.07.014 10.2174/18744443015070101022 10.1109/TMM.2013.2266634 10.1049/iet-cvi.2016.0156 10.1109/TITS.2016.2614548 10.1007/978-3-642-35289-8_26 10.1109/TIP.2011.2182521 10.1109/TIP.2017.2775060 10.1109/TPAMI.2002.1017623 10.1109/TCSVT.2014.2305514 10.1049/iet-cvi.2016.0238 10.1007/s11704-015-4246-3 10.1109/TITS.2013.2263281 10.1109/TIP.2015.2510583 10.1177/0142331214549409 10.1016/j.ijleo.2015.04.031 10.1109/TPAMI.2011.239 10.1049/iet-cvi.2014.0150 10.1109/TCSVT.2012.2226527 10.1109/TPAMI.2005.205 10.1109/TITS.2017.2726546 |
| ContentType | Journal Article |
| Copyright | The Institution of Engineering and Technology 2019 The Institution of Engineering and Technology |
| Copyright_xml | – notice: The Institution of Engineering and Technology – notice: 2019 The Institution of Engineering and Technology |
| DBID | AAYXX CITATION DOA |
| DOI | 10.1049/iet-cvi.2018.5129 |
| DatabaseName | CrossRef DOAJ Directory of Open Access Journals |
| DatabaseTitle | CrossRef |
| DatabaseTitleList | CrossRef |
| Database_xml | – sequence: 1 dbid: DOA name: DOAJ Directory of Open Access Journals url: https://www.doaj.org/ sourceTypes: Open Website |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Applied Sciences |
| EISSN | 1751-9640 |
| EndPage | 164 |
| ExternalDocumentID | oai_doaj_org_article_0bd3caf3d19c46d8a0d758d13e5be8eb 10_1049_iet_cvi_2018_5129 CVI2BF00552 |
| Genre | article |
| GrantInformation_xml | – fundername: Open Fund of State Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University funderid: 17E01 – fundername: Beijing Natural Science Foundation funderid: 4182020 |
| GroupedDBID | 0R 24P 29I 3V. 5GY 6IK 8AL 8FE 8FG 8VB AAJGR ABJCF ABPTK ABUWG ACGFS ACIWK AENEX AFKRA ALMA_UNASSIGNED_HOLDINGS ARAPS AZQEC BENPR BFFAM BGLVJ BPHCQ CS3 DU5 DWQXO EBS EJD ESX GNUQQ HCIFZ HZ IFIPE IPLJI J9A JAVBF K6V K7- L6V LAI LOTEE LXI LXU M0N M43 M7S MS NADUK NXXTH O9- OCL P62 PQEST PQQKQ PQUKI PROAC PTHSS QWB RIE RNS RUI S0W UNMZH UNR ZL0 ZZ .DC 0R~ 0ZK 1OC AAHHS AAHJG ABQXS ACCFJ ACCMX ACESK ACGFO ACXQS ADEYR ADZOD AEEZP AEGXH AEQDE AIWBW AJBDE ALUQN AVUZU CCPQU GROUPED_DOAJ HZ~ IAO ITC K1G MCNEO MS~ OK1 ~ZZ AAMMB AAYXX AEFGJ AFFHD AGXDD AIDQK AIDYY CITATION IDLOA PHGZM PHGZT PQGLB WIN |
| ID | FETCH-LOGICAL-c4002-1f41fc4f978fee81a10c4ba5e6b3a486d98366a344b2b2791f5c92129bff133 |
| IEDL.DBID | 24P |
| ISICitedReferencesCount | 10 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000459454900010&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 1751-9632 1751-9640 |
| IngestDate | Fri Oct 03 12:49:47 EDT 2025 Tue Nov 18 22:00:25 EST 2025 Wed Oct 29 21:08:24 EDT 2025 Wed Jan 22 16:31:57 EST 2025 Tue Jan 05 21:44:11 EST 2021 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 2 |
| Keywords | stacked denoising autoencoders local probability model-based mean shift algorithm probability deep learning-based LPM colour histograms object detection MS tracking algorithm LPM-based mean shift algorithm local probability model feature extraction histograms of oriented gradients hand-crafted features object tracking image colour analysis learning (artificial intelligence) neural nets |
| Language | English |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c4002-1f41fc4f978fee81a10c4ba5e6b3a486d98366a344b2b2791f5c92129bff133 |
| OpenAccessLink | https://doaj.org/article/0bd3caf3d19c46d8a0d758d13e5be8eb |
| PageCount | 8 |
| ParticipantIDs | crossref_citationtrail_10_1049_iet_cvi_2018_5129 iet_journals_10_1049_iet_cvi_2018_5129 doaj_primary_oai_doaj_org_article_0bd3caf3d19c46d8a0d758d13e5be8eb crossref_primary_10_1049_iet_cvi_2018_5129 wiley_primary_10_1049_iet_cvi_2018_5129_CVI2BF00552 |
| ProviderPackageCode | RUI |
| PublicationCentury | 2000 |
| PublicationDate | 20190300 March 2019 2019-03-00 2019-03-01 |
| PublicationDateYYYYMMDD | 2019-03-01 |
| PublicationDate_xml | – month: 3 year: 2019 text: 20190300 |
| PublicationDecade | 2010 |
| PublicationTitle | IET computer vision |
| PublicationYear | 2019 |
| Publisher | The Institution of Engineering and Technology Wiley |
| Publisher_xml | – name: The Institution of Engineering and Technology – name: Wiley |
| References | Shehzad, M.I.; Shah, Y.A.; Mehmood, Z. (C3) 2017; 11 Zoidi, O.; Tefas, A.; Pitas, I.. (C4) 2013; 23 Dou, J.; Li, J. (C21) 2015; 126 Ali, A.; Jalil, A.; Niu, J.W. (C2) 2016; 10 Li, Y.; Zhao, W.; Jiahao, P. (C24) 2017; 14 Li, H.; Li, Y.; Porikli, F.M. (C27) 2015; 25 Yigit, A.; Temizel, A.. (C33) 2017; 11 Chu, C.-T.; Hwang, J.-N.; Pai, H.-I. (C19) 2013; 15 Cao, X.; Gao, C.; Lan, J. (C20) 2014; 124 Kalal, Z.; Krystian, M.; Matas, J. (C28) 2012; 34 Collins, R.T; Liu, Y. (C11) 2003; 27 Wang, L.F.; Yan, H.P.; Wu, H.Y. (C15) 2013; 14 Ali, A.; Jalil, A.; Ahmed, J. (C18) 2015; 9 Song, Y.; Li, S.X.; Zhu, C.F. (C34) 2015; 9 Baochang, Z.; Luan, S.; Chen, C. (C22) 2018; 27 Yuan, Y.; Xiong, Z.; Wang, Q. (C32) 2017; 18 Yang, X.; Di, T.. (C42) 2015; 7 Junyu, G.; Zhang, T.; Yang, X. (C38) 2017; 26 Li, Z.; Yu, X.; Li, P. (C14) 2015; 31 Wang, Q.; Gao, J.; Yuan, Y. (C25) 2018; 19 Wang, J.; Yagi, Y. (C12) 2008; 17 Li, Z.; He, S.; Hashem, M. (C13) 2015; 31 Kim, D.-H.; Kim, H.-K.; Lee, S.J. (C17) 2014; 24 Bengio, Y. (C43) 2012; 7700 Mazinan, A.H. (C35) 2015; 37 Wu, Y.; Cheng, J.; Wang, J. (C9) 2012; 21 Wang, Q.; Wan, J.; Yuan, Y.. (C29) 2017 Tan, X.; Triggs, B. (C8) 2007; 19 Wang, Q.; Wan, J.; Yuan, J. (C30) 2018; 75 Baochang, Z.; Yang, Y.; Chen, C. (C23) 2017; 26 2007; 19 2015; 37 2015; 126 2017; 26 2012 2010 2013; 23 2015; 31 2012; 7700 2008; 17 2016; 10 2009 2008 2007 2006 2014; 24 2005 2002 2015; 9 2012; 34 2015; 7 2018; 27 1999 2018; 19 2015; 25 2013; 15 2013; 14 2017; 14 2017; 11 2003; 27 2017 2017; 18 2014 2013 2018; 75 2012; 21 2014; 124 Yang M.‐H. (e_1_2_6_38_2) 2009 Li H. (e_1_2_6_27_2) 2014 e_1_2_6_31_2 Tan X. (e_1_2_6_9_2) 2007; 19 Wang Q. (e_1_2_6_30_2) 2017 e_1_2_6_18_2 e_1_2_6_19_2 e_1_2_6_12_2 e_1_2_6_35_2 Grabner H. (e_1_2_6_11_2) 2006 e_1_2_6_13_2 e_1_2_6_34_2 e_1_2_6_10_2 e_1_2_6_33_2 Bai Y. (e_1_2_6_37_2) 2012 e_1_2_6_32_2 e_1_2_6_16_2 Junyu G. (e_1_2_6_39_2) 2017; 26 e_1_2_6_17_2 e_1_2_6_14_2 e_1_2_6_15_2 e_1_2_6_36_2 Triggs B. (e_1_2_6_6_2) 2005 e_1_2_6_42_2 e_1_2_6_20_2 e_1_2_6_41_2 e_1_2_6_40_2 e_1_2_6_8_2 e_1_2_6_7_2 e_1_2_6_29_2 e_1_2_6_4_2 e_1_2_6_3_2 e_1_2_6_5_2 e_1_2_6_24_2 e_1_2_6_23_2 e_1_2_6_22_2 e_1_2_6_21_2 e_1_2_6_28_2 e_1_2_6_43_2 Yang M. (e_1_2_6_2_2) 2013 e_1_2_6_44_2 e_1_2_6_26_2 e_1_2_6_25_2 |
| References_xml | – volume: 31 start-page: 1319 issue: 10 year: 2015 end-page: 1337 ident: C13 article-title: Robust object tracking via multi-feature adaptive fusion based on stability: contrast analysis publication-title: Vis. Comput. – volume: 24 start-page: 1288 issue: 8 year: 2014 end-page: 1300 ident: C17 article-title: Kernel-based structural binary pattern tracking publication-title: IEEE Trans. Circuits Syst. Video Technol. – volume: 15 start-page: 1602 issue: 7 year: 2013 end-page: 1615 ident: C19 article-title: Tracking human under occlusion based on adaptive multiple kernels with projected gradients publication-title: IEEE Trans. Multimed. – volume: 19 start-page: 1457 issue: 5 year: 2018 end-page: 1470 ident: C25 article-title: A joint convolutional neural networks and context transfer for street scenes labeling publication-title: IEEE Trans. Intell. Transp. Syst. (T–ITS) – year: 2017 ident: C29 article-title: Deep metric learning for crowdedness regression publication-title: IEEE Trans. Circuits Syst. Video Technol. (T-CSVT) – volume: 37 start-page: 921 issue: 8 year: 2015 end-page: 931 ident: C35 article-title: Performance improvement in artificial intelligence-based objects tracking via probabilistic estimation approach publication-title: Trans. Inst. Meas. Control – volume: 7700 start-page: 437 issue: 1–3 year: 2012 end-page: 478 ident: C43 article-title: Practical recommendations for gradient-based training of deep architectures publication-title: Neural Netw., Tricks Trade – volume: 9 start-page: 489 issue: 4 year: 2015 end-page: 499 ident: C34 article-title: Invariant foreground occupation ratio for scale adaptive mean shift tracking publication-title: IET Comput. Vis. – volume: 7 start-page: 1022 issue: 1 year: 2015 end-page: 1028 ident: C42 article-title: Research on mean-shift target tracking based on image HOG feature publication-title: Open Autom. Control Syst. – volume: 26 start-page: 1845 issue: 99 year: 2017 end-page: 1858 ident: C38 article-title: Deep relative tracking publication-title: IEEE Trans. Image Process. – volume: 19 start-page: 1635 year: 2007 end-page: 1650 ident: C8 article-title: Enhanced local texture feature sets for face recognition under difficult lighting conditions publication-title: IEEE Trans. Image Process. – volume: 10 start-page: 167 issue: 1 year: 2016 end-page: 188 ident: C2 article-title: Visual object tracking–classical and contemporary approaches publication-title: Frontiers Comput. Sci. Sel. Publ. Chin. Univ. – volume: 27 start-page: 1631 issue: 10 year: 2003 end-page: 1643 ident: C11 article-title: Online selection of discriminative tracking features publication-title: IEEE Trans. Pattern Anal. Mach. Intell. – volume: 23 start-page: 870 issue: 5 year: 2013 end-page: 882 ident: C4 article-title: Visual object tracking based on local steering kernels and color histograms publication-title: IEEE Trans. Circuits Syst. Video Technol. – volume: 34 start-page: 1409 issue: 7 year: 2012 end-page: 1422 ident: C28 article-title: Tracking–learning–detection publication-title: IEEE Trans. Pattern Anal. Mach. Intell. – volume: 11 start-page: 255 issue: 3 year: 2017 end-page: 263 ident: C33 article-title: Individual and group tracking with the evaluation of social interactions publication-title: IET Comput. Vis. – volume: 124 start-page: 168 issue: SI year: 2014 end-page: 177 ident: C20 article-title: Ego motion guided particle filter for vehicle tracking in airborne videos publication-title: Neurocomputing – volume: 75 start-page: 272 year: 2018 end-page: 281 ident: C30 article-title: Locality constraint distance metric learning for traffic congestion detection publication-title: Pattern Recognit. (PR) – volume: 126 start-page: 1449 issue: 15–16 year: 2015 end-page: 1456 ident: C21 article-title: Robust visual tracking based on joint multi–feature histogram by integrating particle filter and mean shift publication-title: Optik, Int. J. Light Electron. Opt. – volume: 18 start-page: 1918 issue: 7 year: 2017 end-page: 1929 ident: C32 article-title: An incremental framework for video-based traffic sign detection, tracking and recognition publication-title: IEEE Trans. Intell. Transp. Syst. (T–ITS) – volume: 9 start-page: 1567 issue: 7 year: 2015 end-page: 1585 ident: C18 article-title: Correlation, Kalman filter and adaptive fast mean shift based heuristic approach for robust visual tracking publication-title: Signal Image Video Process. – volume: 26 start-page: 4648 issue: 10 year: 2017 end-page: 4660 ident: C23 article-title: Action recognition using 3D histograms of texture and a multi–class boosting classifier publication-title: IEEE Trans. Image Process. – volume: 14 start-page: 1480 issue: 3 year: 2013 end-page: 1489 ident: C15 article-title: Forward-backward mean-shift for visual tracking with local-background-weighted histogram publication-title: IEEE Trans. Intell. Transp. Syst. – volume: 27 start-page: 1038 year: 2018 end-page: 1048 ident: C22 article-title: Latent constrained correlation filter publication-title: IEEE Trans. Image Process. – volume: 14 start-page: 1256 issue: 2 year: 2017 end-page: 1264 ident: C24 article-title: Deformable patterned fabric defect detection with fisher criterion based deep learning publication-title: IEEE Trans. Autom. Sci. Eng. – volume: 11 start-page: 68 issue: 1 year: 2017 end-page: 77 ident: C3 article-title: –means based multiple objects tracking with long-term occlusion handling publication-title: IET Comput. Vis. – volume: 21 start-page: 2824 issue: 5 year: 2012 end-page: 2837 ident: C9 article-title: Real–time probabilistic covariance tracking with efficient model update publication-title: IEEE Trans. Image Process. – volume: 17 start-page: 235 issue: 2 year: 2008 end-page: 240 ident: C12 article-title: Integrating color and shape-texture features for adaptive real-time object tracking publication-title: IEEE Trans. Image Process. – volume: 31 start-page: 1633 issue: 12 year: 2015 end-page: 1651 ident: C14 article-title: Moving object tracking based on multi-independent features distribution fields with comprehensive spatial feature similarity publication-title: Vis. Comput. – volume: 25 start-page: 1834 issue: 4 year: 2015 end-page: 1848 ident: C27 article-title: Deeptrack: learning discriminative feature representations online for robust visual tracking publication-title: IEEE Trans. Image Process. – volume: 31 start-page: 1633 issue: 12 year: 2015 end-page: 1651 article-title: Moving object tracking based on multi‐independent features distribution fields with comprehensive spatial feature similarity publication-title: Vis. Comput. – volume: 126 start-page: 1449 issue: 15–16 year: 2015 end-page: 1456 article-title: Robust visual tracking based on joint multi–feature histogram by integrating particle filter and mean shift publication-title: Optik, Int. J. Light Electron. Opt. – start-page: 971 year: 2002 end-page: 987 – start-page: 1 year: 2007 end-page: 8 – volume: 31 start-page: 1319 issue: 10 year: 2015 end-page: 1337 article-title: Robust object tracking via multi‐feature adaptive fusion based on stability: contrast analysis publication-title: Vis. Comput. – volume: 15 start-page: 1602 issue: 7 year: 2013 end-page: 1615 article-title: Tracking human under occlusion based on adaptive multiple kernels with projected gradients publication-title: IEEE Trans. Multimed. – volume: 124 start-page: 168 issue: SI year: 2014 end-page: 177 article-title: Ego motion guided particle filter for vehicle tracking in airborne videos publication-title: Neurocomputing – start-page: 1854 year: 2012 end-page: 1861 – volume: 21 start-page: 2824 issue: 5 year: 2012 end-page: 2837 article-title: Real–time probabilistic covariance tracking with efficient model update publication-title: IEEE Trans. Image Process. – volume: 7 start-page: 1022 issue: 1 year: 2015 end-page: 1028 article-title: Research on mean‐shift target tracking based on image HOG feature publication-title: Open Autom. Control Syst. – volume: 24 start-page: 1288 issue: 8 year: 2014 end-page: 1300 article-title: Kernel‐based structural binary pattern tracking publication-title: IEEE Trans. Circuits Syst. Video Technol. – start-page: 393 year: 2014 end-page: 400 – volume: 10 start-page: 167 issue: 1 year: 2016 end-page: 188 article-title: Visual object tracking–classical and contemporary approaches publication-title: Frontiers Comput. Sci. Sel. Publ. Chin. Univ. – start-page: 886 year: 2005 end-page: 893 – start-page: 194 year: 2014 end-page: 209 – volume: 19 start-page: 1635 year: 2007 end-page: 1650 article-title: Enhanced local texture feature sets for face recognition under difficult lighting conditions publication-title: IEEE Trans. Image Process. – start-page: 3789 year: 2010 end-page: 3792 – volume: 23 start-page: 870 issue: 5 year: 2013 end-page: 882 article-title: Visual object tracking based on local steering kernels and color histograms publication-title: IEEE Trans. Circuits Syst. Video Technol. – volume: 9 start-page: 489 issue: 4 year: 2015 end-page: 499 article-title: Invariant foreground occupation ratio for scale adaptive mean shift tracking publication-title: IET Comput. Vis. – volume: 25 start-page: 1834 issue: 4 year: 2015 end-page: 1848 article-title: Deeptrack: learning discriminative feature representations online for robust visual tracking publication-title: IEEE Trans. Image Process. – volume: 27 start-page: 1631 issue: 10 year: 2003 end-page: 1643 article-title: Online selection of discriminative tracking features publication-title: IEEE Trans. Pattern Anal. Mach. Intell. – volume: 11 start-page: 255 issue: 3 year: 2017 end-page: 263 article-title: Individual and group tracking with the evaluation of social interactions publication-title: IET Comput. Vis. – volume: 11 start-page: 68 issue: 1 year: 2017 end-page: 77 article-title: –means based multiple objects tracking with long‐term occlusion handling publication-title: IET Comput. Vis. – start-page: 2411 year: 2013 end-page: 2418 – year: 2017 article-title: Deep metric learning for crowdedness regression publication-title: IEEE Trans. Circuits Syst. Video Technol. (T‐CSVT) – start-page: 983 year: 2009 end-page: 990 – volume: 18 start-page: 1918 issue: 7 year: 2017 end-page: 1929 article-title: An incremental framework for video‐based traffic sign detection, tracking and recognition publication-title: IEEE Trans. Intell. Transp. Syst. (T–ITS) – start-page: 47 year: 2006 end-page: 56 – volume: 17 start-page: 235 issue: 2 year: 2008 end-page: 240 article-title: Integrating color and shape‐texture features for adaptive real‐time object tracking publication-title: IEEE Trans. Image Process. – volume: 9 start-page: 1567 issue: 7 year: 2015 end-page: 1585 article-title: Correlation, Kalman filter and adaptive fast mean shift based heuristic approach for robust visual tracking publication-title: Signal Image Video Process. – volume: 14 start-page: 1256 issue: 2 year: 2017 end-page: 1264 article-title: Deformable patterned fabric defect detection with fisher criterion based deep learning publication-title: IEEE Trans. Autom. Sci. Eng. – start-page: 1208 year: 2009 end-page: 1215 – volume: 34 start-page: 1409 issue: 7 year: 2012 end-page: 1422 article-title: Tracking–learning–detection publication-title: IEEE Trans. Pattern Anal. Mach. Intell. – volume: 75 start-page: 272 year: 2018 end-page: 281 article-title: Locality constraint distance metric learning for traffic congestion detection publication-title: Pattern Recognit. (PR) – volume: 37 start-page: 921 issue: 8 year: 2015 end-page: 931 article-title: Performance improvement in artificial intelligence‐based objects tracking via probabilistic estimation approach publication-title: Trans. Inst. Meas. Control – volume: 27 start-page: 1038 year: 2018 end-page: 1048 article-title: Latent constrained correlation filter publication-title: IEEE Trans. Image Process. – volume: 26 start-page: 4648 issue: 10 year: 2017 end-page: 4660 article-title: Action recognition using 3D histograms of texture and a multi–class boosting classifier publication-title: IEEE Trans. Image Process. – volume: 19 start-page: 1457 issue: 5 year: 2018 end-page: 1470 article-title: A joint convolutional neural networks and context transfer for street scenes labeling publication-title: IEEE Trans. Intell. Transp. Syst. (T–ITS) – start-page: 1096 year: 2008 end-page: 1103 – volume: 7700 start-page: 437 issue: 1–3 year: 2012 end-page: 478 article-title: Practical recommendations for gradient‐based training of deep architectures publication-title: Neural Netw., Tricks Trade – volume: 26 start-page: 1845 issue: 99 year: 2017 end-page: 1858 article-title: Deep relative tracking publication-title: IEEE Trans. Image Process. – volume: 14 start-page: 1480 issue: 3 year: 2013 end-page: 1489 article-title: Forward‐backward mean‐shift for visual tracking with local‐background‐weighted histogram publication-title: IEEE Trans. Intell. Transp. Syst. – year: 1999 – start-page: 194 volume-title: Asian Conference on Computer Vision (ACCV) year: 2014 ident: e_1_2_6_27_2 – ident: e_1_2_6_42_2 doi: 10.1109/CVPRW.2014.126 – ident: e_1_2_6_24_2 doi: 10.1109/TIP.2017.2718189 – ident: e_1_2_6_14_2 doi: 10.1007/s00371-014-1014-6 – ident: e_1_2_6_7_2 doi: 10.1109/ICCV.2007.4408954 – ident: e_1_2_6_40_2 doi: 10.1109/ICCV.1999.790416 – start-page: 47 volume-title: Proceedings of British Machine Vision Conference (BMVC) year: 2006 ident: e_1_2_6_11_2 – ident: e_1_2_6_19_2 doi: 10.1007/s11760-014-0612-0 – ident: e_1_2_6_17_2 doi: 10.1109/CVPR.2009.5206502 – ident: e_1_2_6_13_2 doi: 10.1109/TIP.2007.914150 – ident: e_1_2_6_25_2 doi: 10.1109/TASE.2016.2520955 – ident: e_1_2_6_32_2 doi: 10.1109/ICIP.2010.5653525 – volume: 19 start-page: 1635 year: 2007 ident: e_1_2_6_9_2 article-title: Enhanced local texture feature sets for face recognition under difficult lighting conditions publication-title: IEEE Trans. Image Process. – ident: e_1_2_6_31_2 doi: 10.1016/j.patcog.2017.03.030 – ident: e_1_2_6_41_2 doi: 10.1145/1390156.1390294 – ident: e_1_2_6_15_2 doi: 10.1007/s00371-014-1044-0 – ident: e_1_2_6_21_2 doi: 10.1016/j.neucom.2013.07.014 – ident: e_1_2_6_43_2 doi: 10.2174/18744443015070101022 – start-page: 2411 volume-title: 2013 IEEE Conf. Computer Vision and Pattern Recognition (CVPR) year: 2013 ident: e_1_2_6_2_2 – start-page: 886 volume-title: 2005 IEEE Computer Society Conf. Computer Vision and Pattern Recognition (CVPR) year: 2005 ident: e_1_2_6_6_2 – ident: e_1_2_6_20_2 doi: 10.1109/TMM.2013.2266634 – ident: e_1_2_6_4_2 doi: 10.1049/iet-cvi.2016.0156 – ident: e_1_2_6_33_2 doi: 10.1109/TITS.2016.2614548 – ident: e_1_2_6_44_2 doi: 10.1007/978-3-642-35289-8_26 – ident: e_1_2_6_10_2 doi: 10.1109/TIP.2011.2182521 – ident: e_1_2_6_23_2 doi: 10.1109/TIP.2017.2775060 – ident: e_1_2_6_8_2 doi: 10.1109/TPAMI.2002.1017623 – ident: e_1_2_6_18_2 doi: 10.1109/TCSVT.2014.2305514 – ident: e_1_2_6_34_2 doi: 10.1049/iet-cvi.2016.0238 – start-page: 1854 volume-title: 2012 IEEE Conf. Computer Vision and Pattern Recognition (CVPR) year: 2012 ident: e_1_2_6_37_2 – ident: e_1_2_6_3_2 doi: 10.1007/s11704-015-4246-3 – ident: e_1_2_6_16_2 doi: 10.1109/TITS.2013.2263281 – ident: e_1_2_6_28_2 doi: 10.1109/TIP.2015.2510583 – ident: e_1_2_6_36_2 doi: 10.1177/0142331214549409 – ident: e_1_2_6_22_2 doi: 10.1016/j.ijleo.2015.04.031 – ident: e_1_2_6_29_2 doi: 10.1109/TPAMI.2011.239 – ident: e_1_2_6_35_2 doi: 10.1049/iet-cvi.2014.0150 – ident: e_1_2_6_5_2 doi: 10.1109/TCSVT.2012.2226527 – start-page: 983 volume-title: 2009 IEEE Conf. Computer Vision and Pattern Recognition (CVPR) year: 2009 ident: e_1_2_6_38_2 – year: 2017 ident: e_1_2_6_30_2 article-title: Deep metric learning for crowdedness regression publication-title: IEEE Trans. Circuits Syst. Video Technol. (T‐CSVT) – ident: e_1_2_6_12_2 doi: 10.1109/TPAMI.2005.205 – ident: e_1_2_6_26_2 doi: 10.1109/TITS.2017.2726546 – volume: 26 start-page: 1845 issue: 99 year: 2017 ident: e_1_2_6_39_2 article-title: Deep relative tracking publication-title: IEEE Trans. Image Process. |
| SSID | ssj0056994 |
| Score | 2.220281 |
| Snippet | Object detection and tracking under complex environment are challenging because of the disturbances induced by background clutter, illumination changes,... |
| SourceID | doaj crossref wiley iet |
| SourceType | Open Website Enrichment Source Index Database Publisher |
| StartPage | 157 |
| SubjectTerms | colour histograms deep learning-based LPM feature extraction hand-crafted features histograms of oriented gradients image colour analysis learning (artificial intelligence) local probability model local probability model-based mean shift algorithm LPM-based mean shift algorithm MS tracking algorithm neural nets object detection object tracking probability Special Issue: Visual Domain Adaptation and Generalisation stacked denoising autoencoders |
| SummonAdditionalLinks | – databaseName: DOAJ Directory of Open Access Journals dbid: DOA link: http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV1LS8QwEA4iHrz4FtcXOYgHodomabc56uKi4AsU9RbylAWpslvFoz_B3-gvcSaty3rRi9cyScOXmcwkmcxHyA7XDqKKIBLmuywRNjOJhmlOvLRpCfYkpWnIJroXF-X9vbyaoPrCnLCmPHAD3EFqHLc6cJdJKwpX6tRBiOsy7nPjS29w9U278nsz1azBeSEjBSL4xiwBFWPj-0x5MPB1Yl8HmNVV7qO7--GRYuF-8DMg9TNcjf6mv0Dm2kCRHjYDXCRTvloi823QSFuTHC2Tu0uDRynU-TpmVVVUV47WQ23xEJziG7EhRat_9G904lkbxYz3B2jmn2lLHfHw-f6BXs3Rs6vzFXLdP77pnSQtW0JiRWQoCSILVgTYFgbvy0xnqRVG574wXIuycLLkRaG5EIYZ1pVZyK0ExyVNCLBRXSXT1VPl1wg1UheGAeI5yHqmjZAhmDwU3Gmnc9Yh6TdeyraFxJHP4lHFC20hFYCnAGKFECuEuEP2xk2emyoavwkf4SSMBbEAdvwAaqFatVB_qUWH7GLHrUGOfvsbj7P897hU7_aUHfWxahlb_48xbpBZ6Fw2GW2bZLoevvgtMmNf68FouB0V-gs-Hvq8 priority: 102 providerName: Directory of Open Access Journals |
| Title | Object detection and tracking under Complex environment using deep learning-based LPM |
| URI | http://digital-library.theiet.org/content/journals/10.1049/iet-cvi.2018.5129 https://onlinelibrary.wiley.com/doi/abs/10.1049%2Fiet-cvi.2018.5129 https://doaj.org/article/0bd3caf3d19c46d8a0d758d13e5be8eb |
| Volume | 13 |
| WOSCitedRecordID | wos000459454900010&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 | |
| journalDatabaseRights | – providerCode: PRVWIB databaseName: Wiley Online Library Free Content customDbUrl: eissn: 1751-9640 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0056994 issn: 1751-9632 databaseCode: WIN dateStart: 20130101 isFulltext: true titleUrlDefault: https://onlinelibrary.wiley.com providerName: Wiley-Blackwell – providerCode: PRVWIB databaseName: Wiley Online Library Open Access customDbUrl: eissn: 1751-9640 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0056994 issn: 1751-9632 databaseCode: 24P dateStart: 20130101 isFulltext: true titleUrlDefault: https://authorservices.wiley.com/open-science/open-access/browse-journals.html providerName: Wiley-Blackwell |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV3daxQxEA-1-uCLrV94VUsexAdhdfOxuc2jPTws6Hmg2L6FfB4HZVv2tqWP_RP8G_1LnMnunS1CBfFlH8JMNkzyy0wmkxlCXgkbwKpIsuBxzAvpmSssTHMRtS9rwJPWri82MZ7N6uNjPd8ik_VbmD4_xMbhhsjI-zUC3Lq-CgkYtTCJy9gV_mKJ0Vn1W1Rbd8hdxsQYlzaX8_V2XCmdqyGCmmSFVvL31aZ-90cXN5RTzuEPKgeoblquWfVMd_7LoHfJg8HypO_7pfKQbMXmEdkZrFA6YHz1mBx9ceiboSF2OUyrobYJtGutR686xUdnLcVt5CRe0mvv5CiG0C-ALZ7RoRbF4ufVD1STgX6af35Cvk4_fJt8LIbyC4WXueRJkix5meCcmWKsmWWll85WUTlhZa2CroVSVkjpuONjzVLlNWhC7VKCk-9Tst2cNvEZoU5b5XgSoQLayK2TOiVXJSWCDbbiI1KupW78kJkcC2ScmHxDLrUBsRkQm0GxGRTbiLzZsJz1aTluIz7AqdwQYkbt3HDaLswAUFO6ILyFQTLtpQq1LQMcpQITsXKxjm5EXmPHA8JXt_1N5BXw93GZyfdDfjDFNGh875-4npP70K77mLgXZLtrz-NLcs9fdMtVu59xsJ-9DPA9Opz9AjjrDn0 |
| linkProvider | Wiley-Blackwell |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV1Lb9QwEB6VFgkutLzE8ig-VD0gBRLb8cZHWli1YrtdiQp6s_xcrVSlVTZUHPkJ_EZ-CR4nu6VCKlLFNRo71tjjb2Y8D4Adpl3UKgLPqB_SjNvCZDpuc-alzasoT1KartnEcDKpTk_ldA0-LHNhuvoQK4cbSka6r1HA0SHdGZwci2TOfZvZyzmGZ1VvEbfuwAaPaIN9DCifLu_jUsjUDjHiZJFJwa_eNuW7v6a4hk6piH_EnEh1XXVN2DPa_D-r3oIHve5J3neH5SGs-foRbPZ6KOmlfPEYvh4b9M4Q59sUqFUTXTvSNtqiX51g2llD8CI589_JH5lyBIPoZ3GYvyB9N4rZrx8_ESgdGU-PnsDn0ceT_YOsb8CQWZ6angReBMtDtDSD91Whi9xyo0svDNO8Ek5WTAjNODfU0KEsQmllxEJpQoi271NYr89r_wyIkVoYGpgrI62n2nAZgimDYE47XdIB5Eu2K9vXJscWGWcqvZFzqSLbVGSbQrYpZNsA3qyGXHSFOW4i3sO9XBFiTe304byZqV5EVW4cszouspCWC1fp3EVjyhXMl8ZX3gxgFyfuZXxx099YOgL_Xpfa_3JI90ZYCI0-v9Wo13Dv4ORorMaHk08v4H6kkV2E3EtYb5tv_hXctZftfNFsJ6H4DXuUEFs |
| linkToPdf | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV3daxQxEB9qK-KL9ROvtZqH4oOwuptkc5tH23q02J4HivYt5PM4KNtjby199E_wb_QvaSa7d7UIFcTXZZINk8xXMvMbgF2mXfQqAs-oH9KM28JkOm5z5qXNqyhPUpqu2cRwPK5OT-VkDQ6WtTAdPsTqwg0lI-lrFHA_d6ELODmCZM58m9mLGaZnVW_Rbt2BDV5GXYv4znyy1MelkKkdYrSTRSYFv37blO_-mOKGdUog_tHmRKqbrmuyPaPN_7Pqh_Cg9z3J--6wPII1Xz-Gzd4PJb2UL57At08Gb2eI821K1KqJrh1pG23xXp1g2VlDUJGc-UvyW6UcwST6aRzm56TvRjH99eMnGkpHjicnT-Hz6MOX_cOsb8CQWZ6angReBMtDjDSD91Whi9xyo0svDNO8Ek5WTAjNODfU0KEsQmlltIXShBBj32ewXp_X_jkQI7UwNDBXRlpPteEyBFMGwZx2uqQDyJdsV7bHJscWGWcqvZFzqSLbVGSbQrYpZNsA3qyGzDtgjtuI93AvV4SIqZ0-nDdT1Yuoyo1jVsdFFtJy4SqduxhMuYL50vjKmwG8xol7GV_c9jeWjsDf16X2vx7RvRECodGtfxr1Cu5NDkbq-Gj8cRvuRxLZJci9gPW2-e534K69aGeL5mWSiSsK6w_f |
| 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=Object+detection+and+tracking+under+Complex+environment+using+deep+learning%E2%80%90based+LPM&rft.jtitle=IET+computer+vision&rft.au=Li%2C+Yundong&rft.au=Zhang%2C+Xueyan&rft.au=Li%2C+Hongguang&rft.au=Zhou%2C+Qichen&rft.date=2019-03-01&rft.pub=The+Institution+of+Engineering+and+Technology&rft.issn=1751-9640&rft.eissn=1751-9640&rft.volume=13&rft.issue=2&rft.spage=157&rft.epage=164&rft_id=info:doi/10.1049%2Fiet-cvi.2018.5129&rft.externalDBID=10.1049%252Fiet-cvi.2018.5129&rft.externalDocID=CVI2BF00552 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1751-9632&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1751-9632&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1751-9632&client=summon |