Vehicle re-identification using relative entropy representation and gated recurrent unit networks
Vehicle Re-Identification is a prevailing subject in the field of computer vision technologies. Major problem in vehicle re-id is re identifying the vehicle from different viewpoints with occlusion and low illumination. In this work, we have proposed a deep-learning framework which effectively proce...
Saved in:
| Published in | Procedia computer science Vol. 171; pp. 508 - 513 |
|---|---|
| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
Elsevier B.V
2020
|
| Subjects | |
| Online Access | Get full text |
| ISSN | 1877-0509 1877-0509 |
| DOI | 10.1016/j.procs.2020.04.054 |
Cover
| Abstract | Vehicle Re-Identification is a prevailing subject in the field of computer vision technologies. Major problem in vehicle re-id is re identifying the vehicle from different viewpoints with occlusion and low illumination. In this work, we have proposed a deep-learning framework which effectively processes the latent representation of the vehicle image instances. The key to our framework is GRUs, which is used to determine the relationship between the vehicle image instances over a period of time, without compromising the computational cost, which is also used to encode the data. |
|---|---|
| AbstractList | Vehicle Re-Identification is a prevailing subject in the field of computer vision technologies. Major problem in vehicle re-id is re identifying the vehicle from different viewpoints with occlusion and low illumination. In this work, we have proposed a deep-learning framework which effectively processes the latent representation of the vehicle image instances. The key to our framework is GRUs, which is used to determine the relationship between the vehicle image instances over a period of time, without compromising the computational cost, which is also used to encode the data. |
| Author | Balakrishnan, Ramadoss Gupta, Arpita Chittrarasu, Abilash |
| Author_xml | – sequence: 1 givenname: Arpita surname: Gupta fullname: Gupta, Arpita organization: National Institute of Technology, Tiruchirappalli, 620015, India – sequence: 2 givenname: Abilash surname: Chittrarasu fullname: Chittrarasu, Abilash organization: National Institute of Technology, Tiruchirappalli, 620015, India – sequence: 3 givenname: Ramadoss surname: Balakrishnan fullname: Balakrishnan, Ramadoss email: brama@nitt.edu organization: National Institute of Technology, Tiruchirappalli, 620015, India |
| BookMark | eNqFkL9OwzAQhy1UJErpE7DkBRLOcf7UAwOqgCJVYgFWy7lciktwIjst6tvjEgbEAF7su5-_k-47ZxPbWWLskkPCgRdX26R3HfokhRQSyBLIsxM25YuyjCEHOfnxPmNz77cQjlgsJC-nTL_Qq8GWIkexqckOpjGoB9PZaOeN3YR-G8o9RSFzXX8Ijd6RD9X4S9s62uiB6hDgzrkQRDtrhsjS8NG5N3_BThvdepp_3zP2fHf7tFzF68f7h-XNOsa0EFmMvKozLCpR8axKhS44EfE8LxFFISrdVBK5roEIhQRBaSoFNDpFWQFwLsSMiXEuus57R43qnXnX7qA4qKMotVVfotRRlIJMBVGBkr8oNONmg9Om_Ye9HlkKa-0NOeXRkEWqTXAxqLozf_Kf8FGKrg |
| CitedBy_id | crossref_primary_10_1007_s00521_021_06658_4 |
| Cites_doi | 10.1016/j.sigpro.2008.09.013 10.1109/DT.2017.8012146 10.1109/TMM.2017.2751966 10.1109/ICCV.2017.210 10.1016/j.cviu.2019.03.001 10.3390/s16060825 10.1109/ICPR.2018.8545514 10.1109/CVPRW.2018.00024 10.1007/s11263-015-0816-y 10.1007/978-3-319-46475-6_53 10.1016/j.patcog.2019.01.008 10.1109/34.824819 10.1109/CVPR.2018.00679 10.1109/TCSVT.2017.2734740 10.1109/CVPR.2015.7299023 10.1109/ICCV.2017.49 10.1109/ICME.2016.7553002 10.1109/TITS.2015.2430892 |
| ContentType | Journal Article |
| Copyright | 2020 |
| Copyright_xml | – notice: 2020 |
| DBID | 6I. AAFTH AAYXX CITATION |
| DOI | 10.1016/j.procs.2020.04.054 |
| DatabaseName | ScienceDirect Open Access Titles Elsevier:ScienceDirect:Open Access CrossRef |
| DatabaseTitle | CrossRef |
| DatabaseTitleList | |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Computer Science |
| EISSN | 1877-0509 |
| EndPage | 513 |
| ExternalDocumentID | 10_1016_j_procs_2020_04_054 S1877050920310218 |
| GroupedDBID | --K 0R~ 0SF 1B1 457 5VS 6I. 71M AACTN AAEDT AAEDW AAFTH AAIKJ AALRI AAQFI AAXUO ABMAC ACGFS ADBBV ADEZE AEXQZ AFTJW AGHFR AITUG ALMA_UNASSIGNED_HOLDINGS AMRAJ E3Z EBS EJD EP3 FDB FNPLU HZ~ IXB KQ8 M41 M~E NCXOZ O-L O9- OK1 P2P RIG ROL SES SSZ AAYWO AAYXX ABWVN ACRPL ACVFH ADCNI ADNMO ADVLN AEUPX AFPUW AIGII AKBMS AKRWK AKYEP CITATION |
| ID | FETCH-LOGICAL-c2634-c1bd4c6b3b14b23a61eee1557cc363bafb9c1ad0eec3903e22930fa2c9b001133 |
| IEDL.DBID | IXB |
| ISSN | 1877-0509 |
| IngestDate | Tue Jul 01 02:53:35 EDT 2025 Thu Apr 24 22:53:46 EDT 2025 Tue May 16 22:33:23 EDT 2023 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Keywords | Vehicle Re-Identification GRU VAE Deep Learning |
| Language | English |
| License | This is an open access article under the CC BY-NC-ND license. |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c2634-c1bd4c6b3b14b23a61eee1557cc363bafb9c1ad0eec3903e22930fa2c9b001133 |
| OpenAccessLink | https://www.sciencedirect.com/science/article/pii/S1877050920310218 |
| PageCount | 6 |
| ParticipantIDs | crossref_primary_10_1016_j_procs_2020_04_054 crossref_citationtrail_10_1016_j_procs_2020_04_054 elsevier_sciencedirect_doi_10_1016_j_procs_2020_04_054 |
| ProviderPackageCode | CITATION AAYXX |
| PublicationCentury | 2000 |
| PublicationDate | 2020 2020-00-00 |
| PublicationDateYYYYMMDD | 2020-01-01 |
| PublicationDate_xml | – year: 2020 text: 2020 |
| PublicationDecade | 2020 |
| PublicationTitle | Procedia computer science |
| PublicationYear | 2020 |
| Publisher | Elsevier B.V |
| Publisher_xml | – name: Elsevier B.V |
| References | Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T... & Adam, H. (2017). Mobilenets: Efficient convolutional neural networks for mobile vision applications. arXiv preprint arXiv:1704.04861. Liu, Xinchen, Wu Liu, Tao Mei, and Huadong Ma. A deep learning-based approach to progressive vehicle re-identification for urban surveillance. In European Conference on Computer Vision, pp. 869-884. Springer, Cham, 2016. Prinsloo, Malekian (bib0008) 2016; 16 Khan, Ullah (bib0007) 2019; 182 Liu, X., Liu, W., Ma, H., & Fu, H. (2016, July). Large-scale vehicle re-identification in urban surveillance videos. In 2016 IEEE International Conference on Multimedia and Expo (ICME) (pp. 1-6). IEEE. Zhou, Shao (bib00020) 2017; 1 bib0006 Gazzah, Sami, N. Essoukri, and B. Amara. "Vehicle Re-identification in Camera Networks: A Review and New Perspectives." In Proceedings of the ACIT’2017 The International Arab Conference on Information Technology, Yassmine Hammamet, Tunisia, pp. 22-24. 2017. Hu, Bai, Qi, Chen, Xue, Mei (bib00012) 2015; 16 Zhu, Zeng, Liao, Lei, Cai, Zheng (bib0004) 2017; 28 Zhou, Y., & Shao, L. (2018). Aware attentive multi-view inference for vehicle re-identification. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 6489-6498). Jain, Duin, Mao (bib0002) 2000; 22 Russakovsky, Deng, Su, Krause, Satheesh, Ma, Berg (bib00015) 2015; 115 Y. Zhouy and L. Shao. (2018). Viewpoint-aware attentive multi-view inference for vehicle re-identification. In 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Yang, L., Luo, P., Change Loy, C., & Tang, X. (2015). A large-scale car dataset for fine-grained categorization and verification. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 3973-3981). Zhu, J., Zeng, H., Huang, J., Liao, S., Lei, Z., Cai, C., & Zheng, L. (2019). Vehicle re-identification using quadruple directional deep learning features. IEEE Transactions on Intelligent Transportation Systems. Wang, Z., Tang, L., Liu, X., Yao, Z., Yi, S., Shao, J... & Wang, X. (2017). Orientation invariant feature embedding and spatial temporal regularization for vehicle re-identification. In Proceedings of the IEEE International Conference on Computer Vision (pp. 379-387). bib0001 Alfasly, S.A.S., Hu, Y., Liang, T., Jin, X., Zhao, Q., & Liu, B. (2019). Variational Representation Learning for Vehicle Re-Identification. arXiv preprint arXiv:1905.02343. Shen, Y., Xiao, T., Li, H., Yi, S., & Wang, X. (2017). Learning deep neural networks for vehicle re-id with visual-spatio-temporal path proposals. In Proceedings of the IEEE International Conference on Computer Vision (pp. 1900-1909). Liu, Liu, Mei, Ma (bib0009) 2017; 20 Bashir, Shahzad, Fraz (bib00023) 2019; 90 Wu, C.W., Liu, C.T., Chiang, C.E., Tu, W.C., & Chien, S.Y. (2018). Vehicle re-identification with the space-time prior. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops (pp. 121-128). Gianfelici, Turchetti, Crippa (bib0003) 2009; 89 10.1016/j.procs.2020.04.054_bib00018 10.1016/j.procs.2020.04.054_bib00017 Prinsloo (10.1016/j.procs.2020.04.054_bib0008) 2016; 16 Liu (10.1016/j.procs.2020.04.054_bib0009) 2017; 20 10.1016/j.procs.2020.04.054_bib00019 10.1016/j.procs.2020.04.054_bib00014 10.1016/j.procs.2020.04.054_bib00013 10.1016/j.procs.2020.04.054_bib00024 10.1016/j.procs.2020.04.054_bib00016 Khan (10.1016/j.procs.2020.04.054_bib0007) 2019; 182 Hu (10.1016/j.procs.2020.04.054_bib00012) 2015; 16 Gianfelici (10.1016/j.procs.2020.04.054_bib0003) 2009; 89 Zhu (10.1016/j.procs.2020.04.054_bib0004) 2017; 28 Jain (10.1016/j.procs.2020.04.054_bib0002) 2000; 22 Russakovsky (10.1016/j.procs.2020.04.054_bib00015) 2015; 115 10.1016/j.procs.2020.04.054_bib0006 10.1016/j.procs.2020.04.054_bib00010 Zhou (10.1016/j.procs.2020.04.054_bib00020) 2017; 1 10.1016/j.procs.2020.04.054_bib00021 10.1016/j.procs.2020.04.054_bib0005 10.1016/j.procs.2020.04.054_bib00011 10.1016/j.procs.2020.04.054_bib00022 Bashir (10.1016/j.procs.2020.04.054_bib00023) 2019; 90 10.1016/j.procs.2020.04.054_bib0001 |
| References_xml | – reference: Zhu, J., Zeng, H., Huang, J., Liao, S., Lei, Z., Cai, C., & Zheng, L. (2019). Vehicle re-identification using quadruple directional deep learning features. IEEE Transactions on Intelligent Transportation Systems. – reference: Gazzah, Sami, N. Essoukri, and B. Amara. "Vehicle Re-identification in Camera Networks: A Review and New Perspectives." In Proceedings of the ACIT’2017 The International Arab Conference on Information Technology, Yassmine Hammamet, Tunisia, pp. 22-24. 2017. – reference: Shen, Y., Xiao, T., Li, H., Yi, S., & Wang, X. (2017). Learning deep neural networks for vehicle re-id with visual-spatio-temporal path proposals. In Proceedings of the IEEE International Conference on Computer Vision (pp. 1900-1909). – reference: Wu, C.W., Liu, C.T., Chiang, C.E., Tu, W.C., & Chien, S.Y. (2018). Vehicle re-identification with the space-time prior. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops (pp. 121-128). – volume: 16 start-page: 825 year: 2016 ident: bib0008 article-title: Accurate vehicle location system using RFID, an internet of things approach publication-title: Sensors – volume: 115 start-page: 211 year: 2015 end-page: 252 ident: bib00015 article-title: Imagenet large scale visual recognition challenge publication-title: International journal of computer vision – volume: 90 start-page: 52 year: 2019 end-page: 65 ident: bib00023 article-title: Vr-proud: Vehicle re-identification using progressive unsupervised deep architecture publication-title: Pattern Recognition – reference: Y. Zhouy and L. Shao. (2018). Viewpoint-aware attentive multi-view inference for vehicle re-identification. In 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. – ident: bib0006 – reference: Zhou, Y., & Shao, L. (2018). Aware attentive multi-view inference for vehicle re-identification. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 6489-6498). – volume: 16 start-page: 2925 year: 2015 end-page: 2934 ident: bib00012 article-title: Vehicle color recognition with spatial pyramid deep learning publication-title: IEEE Transactions on Intelligent Transportation Systems – reference: Yang, L., Luo, P., Change Loy, C., & Tang, X. (2015). A large-scale car dataset for fine-grained categorization and verification. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 3973-3981). – reference: Liu, X., Liu, W., Ma, H., & Fu, H. (2016, July). Large-scale vehicle re-identification in urban surveillance videos. In 2016 IEEE International Conference on Multimedia and Expo (ICME) (pp. 1-6). IEEE. – volume: 20 start-page: 645 year: 2017 end-page: 658 ident: bib0009 article-title: Provid: Progressive and multimodal vehicle reidentification for large-scale urban surveillance publication-title: IEEE Transactions on Multimedia – ident: bib0001 – volume: 22 start-page: 4 year: 2000 end-page: 37 ident: bib0002 article-title: Statistical pattern recognition: A review publication-title: IEEE Transactions on pattern analysis and machine intelligence – volume: 89 start-page: 422 year: 2009 end-page: 437 ident: bib0003 article-title: A non-probabilistic recognizer of stochastic signals based on KLT publication-title: Signal Processing – reference: Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T... & Adam, H. (2017). Mobilenets: Efficient convolutional neural networks for mobile vision applications. arXiv preprint arXiv:1704.04861. – volume: 182 start-page: 50 year: 2019 end-page: 63 ident: bib0007 article-title: A survey of advances in vision-based vehicle re-identification publication-title: Computer Vision and Image Understanding – reference: Liu, Xinchen, Wu Liu, Tao Mei, and Huadong Ma. A deep learning-based approach to progressive vehicle re-identification for urban surveillance. In European Conference on Computer Vision, pp. 869-884. Springer, Cham, 2016. – volume: 1 start-page: 1 year: 2017 end-page: 12 ident: bib00020 article-title: Cross-View GAN Based Vehicle Generation for Re-identification publication-title: BMVC – reference: Wang, Z., Tang, L., Liu, X., Yao, Z., Yi, S., Shao, J... & Wang, X. (2017). Orientation invariant feature embedding and spatial temporal regularization for vehicle re-identification. In Proceedings of the IEEE International Conference on Computer Vision (pp. 379-387). – volume: 28 start-page: 3183 year: 2017 end-page: 3193 ident: bib0004 article-title: Deep hybrid similarity learning for person re-identification publication-title: IEEE Transactions on Circuits and Systems for Video Technology – reference: Alfasly, S.A.S., Hu, Y., Liang, T., Jin, X., Zhao, Q., & Liu, B. (2019). Variational Representation Learning for Vehicle Re-Identification. arXiv preprint arXiv:1905.02343. – volume: 89 start-page: 422 issue: 4 year: 2009 ident: 10.1016/j.procs.2020.04.054_bib0003 article-title: A non-probabilistic recognizer of stochastic signals based on KLT publication-title: Signal Processing doi: 10.1016/j.sigpro.2008.09.013 – ident: 10.1016/j.procs.2020.04.054_bib0005 doi: 10.1109/DT.2017.8012146 – volume: 20 start-page: 645 issue: 3 year: 2017 ident: 10.1016/j.procs.2020.04.054_bib0009 article-title: Provid: Progressive and multimodal vehicle reidentification for large-scale urban surveillance publication-title: IEEE Transactions on Multimedia doi: 10.1109/TMM.2017.2751966 – ident: 10.1016/j.procs.2020.04.054_bib00018 doi: 10.1109/ICCV.2017.210 – volume: 182 start-page: 50 year: 2019 ident: 10.1016/j.procs.2020.04.054_bib0007 article-title: A survey of advances in vision-based vehicle re-identification publication-title: Computer Vision and Image Understanding doi: 10.1016/j.cviu.2019.03.001 – volume: 16 start-page: 825 issue: 6 year: 2016 ident: 10.1016/j.procs.2020.04.054_bib0008 article-title: Accurate vehicle location system using RFID, an internet of things approach publication-title: Sensors doi: 10.3390/s16060825 – ident: 10.1016/j.procs.2020.04.054_bib0001 doi: 10.1109/ICPR.2018.8545514 – ident: 10.1016/j.procs.2020.04.054_bib00011 doi: 10.1109/CVPRW.2018.00024 – volume: 115 start-page: 211 issue: 3 year: 2015 ident: 10.1016/j.procs.2020.04.054_bib00015 article-title: Imagenet large scale visual recognition challenge publication-title: International journal of computer vision doi: 10.1007/s11263-015-0816-y – ident: 10.1016/j.procs.2020.04.054_bib00019 doi: 10.1007/978-3-319-46475-6_53 – volume: 90 start-page: 52 year: 2019 ident: 10.1016/j.procs.2020.04.054_bib00023 article-title: Vr-proud: Vehicle re-identification using progressive unsupervised deep architecture publication-title: Pattern Recognition doi: 10.1016/j.patcog.2019.01.008 – ident: 10.1016/j.procs.2020.04.054_bib00024 – volume: 22 start-page: 4 issue: 1 year: 2000 ident: 10.1016/j.procs.2020.04.054_bib0002 article-title: Statistical pattern recognition: A review publication-title: IEEE Transactions on pattern analysis and machine intelligence doi: 10.1109/34.824819 – ident: 10.1016/j.procs.2020.04.054_bib00022 doi: 10.1109/CVPR.2018.00679 – ident: 10.1016/j.procs.2020.04.054_bib0006 – volume: 28 start-page: 3183 issue: 11 year: 2017 ident: 10.1016/j.procs.2020.04.054_bib0004 article-title: Deep hybrid similarity learning for person re-identification publication-title: IEEE Transactions on Circuits and Systems for Video Technology doi: 10.1109/TCSVT.2017.2734740 – ident: 10.1016/j.procs.2020.04.054_bib00016 – volume: 1 start-page: 1 year: 2017 ident: 10.1016/j.procs.2020.04.054_bib00020 article-title: Cross-View GAN Based Vehicle Generation for Re-identification publication-title: BMVC – ident: 10.1016/j.procs.2020.04.054_bib00010 doi: 10.1109/CVPR.2018.00679 – ident: 10.1016/j.procs.2020.04.054_bib00013 doi: 10.1109/CVPR.2015.7299023 – ident: 10.1016/j.procs.2020.04.054_bib00021 doi: 10.1109/ICCV.2017.49 – ident: 10.1016/j.procs.2020.04.054_bib00014 – ident: 10.1016/j.procs.2020.04.054_bib00017 doi: 10.1109/ICME.2016.7553002 – volume: 16 start-page: 2925 issue: 5 year: 2015 ident: 10.1016/j.procs.2020.04.054_bib00012 article-title: Vehicle color recognition with spatial pyramid deep learning publication-title: IEEE Transactions on Intelligent Transportation Systems doi: 10.1109/TITS.2015.2430892 |
| SSID | ssj0000388917 |
| Score | 2.1742318 |
| Snippet | Vehicle Re-Identification is a prevailing subject in the field of computer vision technologies. Major problem in vehicle re-id is re identifying the vehicle... |
| SourceID | crossref elsevier |
| SourceType | Enrichment Source Index Database Publisher |
| StartPage | 508 |
| SubjectTerms | Deep Learning GRU VAE Vehicle Re-Identification |
| Title | Vehicle re-identification using relative entropy representation and gated recurrent unit networks |
| URI | https://dx.doi.org/10.1016/j.procs.2020.04.054 |
| Volume | 171 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV1LT8MwDI6mceHCGzEeUw4cidYmWdoex8Q0McEBGOxW5VUYQt00NiT-PXGaIpDQDvTW1paqL4ntWvZnhM6p8-lxIiURwhrCVSKIMoYTdyiZdRFdojzZ882tGI759aQ7aaB-3QsDZZXB9lc23Vvr8KQT0OzMp9POfZwmCbCXUGC3dJ7K2WHoKoUmvsnld54F2E4yP3gX5Ako1ORDvswL_ATQdtPIU552-d8O6ofTGeygrRAt4l71QbuoYcs9tF1PYsDhYO4j-WhfQAIvLJmaUADkMcdQ2P6Mq5aVD4shmTubf2JPZlk3HpVYlgZDQs24F7qibMIrd9pxWZWJvx-g8eDqoT8kYXgC0VQwTnSsDNdCMRVzRZkUsbXWBQ-J1kwwJQuV6ViayFrNsohZ6vx-VEiqgSQxdn-uh6hZzkp7hLBbyyTLNNC0pNxdWcpNWri4gBapEYy2EK0Ry3VgFocBF295XUL2mnuYc4A5j3juYG6hi2-leUWssV5c1EuR_9ofuTP96xSP_6t4gjbhrkq3nKLmcrGyZy4AWao22uiN7p5Gbb_TvgDlhNvH |
| linkProvider | Elsevier |
| linkToHtml | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnZ1LTwIxEIAbxINefBvx2YNHG3bb0t09KpGAAhfBcGu2DxRjFoJg4r-3090lmhgO7nHbSTbTdmZ2Mv0GoWvqfHoYpSkRwhrCVSSIMoYTdyiZdRFdpDzsudcX7SF_GDVGFdQs78JAWWVh-3Ob7q118aZeaLM-m0zqT2EcRUAvoUC3dJ5qA226aCCArd0Z3a0SLYA7SXznXRAgIFHSh3ydFzgK4HbTwDNPG_xvD_XD67T20E4RLuLb_Iv2UcVmB2i3bMWAi5N5iNJn-woz8NySiSkqgLzSMVS2v-D8zsqnxZDNnc6-sKdZljePMpxmBkNGzbgBnTOb8NIdd5zldeIfR2jYuh8026TonkA0FYwTHSrDtVBMhVxRlorQWuuih0hrJphKxyrRYWoCazVLAmapc_zBOKUaKImh-3U9RtVsmtkThN1iRkmigdMSc_ckMTfx2AUGdBwbwWgN0VJjUhdocehw8S7LGrI36dUsQc0y4NKpuYZuVkKznKyxfrool0L-2iDS2f51gqf_FbxCW-1Bryu7nf7jGdqGkTz3co6qi_nSXrhoZKEu_W77Bhfl3Uc |
| 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=Vehicle+re-identification+using+relative+entropy+representation+and+gated+recurrent+unit+networks&rft.jtitle=Procedia+computer+science&rft.au=Gupta%2C+Arpita&rft.au=Chittrarasu%2C+Abilash&rft.au=Balakrishnan%2C+Ramadoss&rft.date=2020&rft.pub=Elsevier+B.V&rft.issn=1877-0509&rft.eissn=1877-0509&rft.volume=171&rft.spage=508&rft.epage=513&rft_id=info:doi/10.1016%2Fj.procs.2020.04.054&rft.externalDocID=S1877050920310218 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1877-0509&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1877-0509&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1877-0509&client=summon |