{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,17]],"date-time":"2026-03-17T21:26:22Z","timestamp":1773782782465,"version":"3.50.1"},"reference-count":51,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2022,12,12]],"date-time":"2022-12-12T00:00:00Z","timestamp":1670803200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,12,12]],"date-time":"2022-12-12T00:00:00Z","timestamp":1670803200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Front. Comput. Sci."],"published-print":{"date-parts":[[2023,8]]},"DOI":"10.1007\/s11704-022-2050-4","type":"journal-article","created":{"date-parts":[[2022,12,12]],"date-time":"2022-12-12T09:03:21Z","timestamp":1670835801000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["An efficient deep learning-assisted person re-identification solution for intelligent video surveillance in smart cities"],"prefix":"10.1007","volume":"17","author":[{"given":"Muazzam","family":"Maqsood","sequence":"first","affiliation":[]},{"given":"Sadaf","family":"Yasmin","sequence":"additional","affiliation":[]},{"given":"Saira","family":"Gillani","sequence":"additional","affiliation":[]},{"given":"Maryam","family":"Bukhari","sequence":"additional","affiliation":[]},{"given":"Seungmin","family":"Rho","sequence":"additional","affiliation":[]},{"given":"Sang-Soo","family":"Yeo","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,12,12]]},"reference":[{"key":"2050_CR1","doi-asserted-by":"publisher","first-page":"25","DOI":"10.1016\/j.cities.2013.12.010","volume":"38","author":"P Neirotti","year":"2014","unstructured":"Neirotti P, De Marco A, Cagliano A C, Mangano G, Scorrano F. Current trends in smart city initiatives: some stylised facts. Cities, 2014, 38: 25\u201336","journal-title":"Cities"},{"issue":"6","key":"2050_CR2","doi-asserted-by":"publisher","first-page":"102","DOI":"10.1109\/MCOM.2013.6525602","volume":"51","author":"P Vlacheas","year":"2013","unstructured":"Vlacheas P, Giaffreda R, Stavroulaki V, Kelaidonis D, Foteinos V, Poulios G, Demestichas P, Somov A, Biswas A R, Moessner K. Enabling smart cities through a cognitive management framework for the internet of things. IEEE Communications Magazine, 2013, 51(6): 102\u2013111","journal-title":"IEEE Communications Magazine"},{"issue":"4","key":"2050_CR3","doi-asserted-by":"publisher","first-page":"61","DOI":"10.3390\/fi12040061","volume":"12","author":"P Singh","year":"2020","unstructured":"Singh P, Nayyar A, Kaur A, Ghosh U. Blockchain and fog based architecture for internet of everything in smart cities. Future Internet, 2020, 12(4): 61","journal-title":"Future Internet"},{"key":"2050_CR4","unstructured":"Zheng L, Yang Y, Hauptmann A G. Person re-identification: past, present and future. 2016, arXiv preprint arXiv: 1610.02984"},{"key":"2050_CR5","doi-asserted-by":"publisher","first-page":"354","DOI":"10.1016\/j.neucom.2019.01.079","volume":"337","author":"D Wu","year":"2019","unstructured":"Wu D, Zheng S J, Zhang X P, Yuan C A, Cheng F, Zhao Y, Lin Y J, Zhao Z-Q, Jiang Y L, Huang D S. Deep learning-based methods for person re-identification: a comprehensive review. Neurocomputing, 2019, 337: 354\u2013371","journal-title":"Neurocomputing"},{"key":"2050_CR6","doi-asserted-by":"crossref","unstructured":"Zahra A, Perwaiz N, Shahzad M, Fraz M M. Person re-identification: a retrospective on domain specific open challenges and future trends. 2022, arXiv preprint arXiv: 2202.13121","DOI":"10.1016\/j.patcog.2023.109669"},{"issue":"6","key":"2050_CR7","doi-asserted-by":"publisher","first-page":"2872","DOI":"10.1109\/TPAMI.2021.3054775","volume":"44","author":"M Ye","year":"2022","unstructured":"Ye M, Shen J, Lin G, Xiang T, Shao L, Hoi S C H. Deep learning for person re-identification: a survey and outlook. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022, 44(6): 2872\u20132893","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"2050_CR8","doi-asserted-by":"publisher","first-page":"107424","DOI":"10.1016\/j.patcog.2020.107424","volume":"110","author":"W Wu","year":"2021","unstructured":"Wu W, Tao D, Li H, Yang Z, Cheng J. Deep features for person re-identification on metric learning. Pattern Recognition, 2021, 110: 107424","journal-title":"Pattern Recognition"},{"issue":"11","key":"2050_CR9","doi-asserted-by":"publisher","first-page":"289","DOI":"10.3390\/fi13110289","volume":"13","author":"X Chen","year":"2021","unstructured":"Chen X, Xu H, Li Y, Bian M. Person re-identification by low-dimensional features and metric learning. Future Internet, 2021, 13(11): 289","journal-title":"Future Internet"},{"issue":"3","key":"2050_CR10","doi-asserted-by":"publisher","first-page":"1479","DOI":"10.1007\/s10489-020-01880-4","volume":"51","author":"R Li","year":"2021","unstructured":"Li R, Zhang B, Teng Z, Fan J. A divide-and-unite deep network for person re-identification. Applied Intelligence, 2021, 51(3): 1479\u20131491","journal-title":"Applied Intelligence"},{"key":"2050_CR11","doi-asserted-by":"publisher","first-page":"104394","DOI":"10.1016\/j.imavis.2022.104394","volume":"119","author":"Z Ming","year":"2022","unstructured":"Ming Z, Zhu M, Wang X, Zhu J, Cheng J, Gao C, Yang Y, Wei X. Deep learning-based person re-identification methods: a survey and outlook of recent works. Image and Vision Computing, 2022, 119: 104394","journal-title":"Image and Vision Computing"},{"key":"2050_CR12","doi-asserted-by":"publisher","first-page":"21","DOI":"10.1007\/978-3-030-32583-1_2","volume-title":"Deep Biometrics","author":"S Lin","year":"2020","unstructured":"Lin S, Li C T. Person re-identification with soft biometrics through deep learning. In: Jiang R, Li C T, Crookes D, Meng W, Rosenberger C, eds. Deep Biometrics. Cham: Springer, 2020, 21\u201336"},{"issue":"6","key":"2050_CR13","doi-asserted-by":"publisher","first-page":"2835","DOI":"10.3390\/app12062835","volume":"12","author":"N Shoukry","year":"2022","unstructured":"Shoukry N, Abd El Ghany MA, Salem M A M. Multi-modal long-term person re-identification using physical soft bio-metrics and body figure. Applied Sciences, 2022, 12(6): 2835","journal-title":"Applied Sciences"},{"issue":"2","key":"2050_CR14","doi-asserted-by":"publisher","first-page":"33","DOI":"10.1145\/3243043","volume":"52","author":"A Nambiar","year":"2020","unstructured":"Nambiar A, Bernardino A, Nascimento J C. Gait-based person re-identification: a survey. ACM Computing Surveys, 2020, 52(2): 33","journal-title":"ACM Computing Surveys"},{"key":"2050_CR15","doi-asserted-by":"crossref","unstructured":"Woo S, Park J, Lee J-Y, Kweon I S. CBAM: convolutional block attention module. In: Proceedings of the 15th European Conference on Computer Vision. 2018, 3\u201319","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"2050_CR16","doi-asserted-by":"crossref","unstructured":"Li W, Zhao R, Xiao T, Wang X DeepReID: deep filter pairing neural network for person re-identification. In: Proceedings of 2014 IEEE Conference on Computer Vision and Pattern Recognition. 2014, 152\u2013159","DOI":"10.1109\/CVPR.2014.27"},{"key":"2050_CR17","doi-asserted-by":"crossref","unstructured":"Ahmed E, Jones M, Marks T K. An improved deep learning architecture for person re-identification. In: Proceedings of 2015 IEEE Conference on Computer Vision and Pattern Recognition. 2015, 3908\u20133916","DOI":"10.1109\/CVPR.2015.7299016"},{"issue":"3","key":"2050_CR18","doi-asserted-by":"publisher","first-page":"802","DOI":"10.1109\/TMI.2016.2629462","volume":"36","author":"S Chen","year":"2017","unstructured":"Chen S, Qin J, Ji X, Lei B, Wang T, Ni D, Cheng J-Z. Automatic scoring of multiple semantic attributes with multi-task feature leverage: a study on pulmonary nodules in CT images. IEEE Transactions on Medical Imaging, 2017, 36(3): 802\u2013814","journal-title":"IEEE Transactions on Medical Imaging"},{"key":"2050_CR19","doi-asserted-by":"publisher","first-page":"191","DOI":"10.1016\/j.neucom.2017.02.055","volume":"241","author":"Y Huang","year":"2017","unstructured":"Huang Y, Sheng H, Zheng Y, Xiong Z. DeepDiff: learning deep difference features on human body parts for person re-identification. Neurocomputing, 2017, 241: 191\u2013203","journal-title":"Neurocomputing"},{"key":"2050_CR20","doi-asserted-by":"crossref","unstructured":"Zhao H, Tian M, Sun S, Shao J, Yan J, Yi S, Wang X, Tang X. Spindle net: person re-identification with human body region guided feature decomposition and fusion. In: Proceedings of 2017 IEEE Conference on Computer Vision and Pattern Recognition. 2017, 907\u2013915","DOI":"10.1109\/CVPR.2017.103"},{"key":"2050_CR21","unstructured":"Hermans A, Beyer L, Leibe B. In defense of the triplet loss for person re-identification. 2017, arXiv preprint arXiv: 1703.07737"},{"issue":"5","key":"2050_CR22","doi-asserted-by":"publisher","first-page":"5863","DOI":"10.1007\/s11042-018-6408-4","volume":"78","author":"Z He","year":"2019","unstructured":"He Z, Jung C, Fu Q, Zhang Z. Deep feature embedding learning for person re-identification based on lifted structured loss. Multimedia Tools and Applications, 2019, 78(5): 5863\u20135880","journal-title":"Multimedia Tools and Applications"},{"key":"2050_CR23","doi-asserted-by":"publisher","first-page":"727","DOI":"10.1016\/j.patcog.2017.10.004","volume":"76","author":"L Wu","year":"2018","unstructured":"Wu L, Wang Y, Li X, Gao J. What-and-where to match: deep spatially multiplicative integration networks for person re-identification. Pattern Recognition, 2018, 76: 727\u2013738","journal-title":"Pattern Recognition"},{"key":"2050_CR24","doi-asserted-by":"crossref","unstructured":"Chatfield K, Simonyan K, Vedaldi A, Zisserman A. Return of the devil in the details: delving deep into convolutional nets. In: Proceedings of British Machine Vision Conference. 2014","DOI":"10.5244\/C.28.6"},{"key":"2050_CR25","unstructured":"Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition. In: Proceedings of the 3rd International Conference on Learning Representations. 2015"},{"issue":"7","key":"2050_CR26","first-page":"2081","volume":"30","author":"L Wu","year":"2020","unstructured":"Wu L, Hong R, Wang Y, Wang M. Cross-entropy adversarial view adaptation for person re-identification. IEEE Transactions on Circuits and Systems for Video Technology, 2020, 30(7): 2081\u20132092","journal-title":"IEEE Transactions on Circuits and Systems for Video Technology"},{"key":"2050_CR27","doi-asserted-by":"crossref","unstructured":"Zhu X, Liu J, Xie H, Zha Z-J. Adaptive alignment network for person re-identification. In: Proceedings of the 25th International Conference on Multimedia Modeling. 2019, 16\u201327","DOI":"10.1007\/978-3-030-05716-9_2"},{"issue":"6","key":"2050_CR28","doi-asserted-by":"publisher","first-page":"2588","DOI":"10.1109\/TIP.2017.2675201","volume":"26","author":"A Wu","year":"2017","unstructured":"Wu A, Zheng W-S, Lai J-H. Robust depth-based person re-identification. IEEE Transactions on Image Processing, 2017, 26(6): 2588\u20132603","journal-title":"IEEE Transactions on Image Processing"},{"issue":"9","key":"2050_CR29","doi-asserted-by":"publisher","first-page":"092108","DOI":"10.1007\/s11432-016-9086-8","volume":"61","author":"Z Imani","year":"2018","unstructured":"Imani Z, Soltanizadeh H. Histogram of the node strength and histogram of the edge weight: two new features for RGB-D person re-identification. Science China Information Sciences, 2018, 61(9): 092108","journal-title":"Science China Information Sciences"},{"key":"2050_CR30","doi-asserted-by":"publisher","first-page":"446","DOI":"10.1016\/j.patcog.2017.06.037","volume":"72","author":"L Ren","year":"2017","unstructured":"Ren L, Lu J, Feng J, Zhou J. Multi-modal uniform deep learning for RGB-D person re-identification. Pattern Recognition, 2017, 72: 446\u2013457","journal-title":"Pattern Recognition"},{"key":"2050_CR31","doi-asserted-by":"crossref","unstructured":"Wu A, Zheng W-S, Yu H-X, Gong S, Lai J. RGB-infrared cross-modality person re-identification. In: Proceedings of 2017 IEEE International Conference on Computer Vision. 2017, 5390\u20135399","DOI":"10.1109\/ICCV.2017.575"},{"key":"2050_CR32","doi-asserted-by":"crossref","unstructured":"M\u00f8gelmose A, Bahnsen C, Moeslund T B, Clapes A, Escalera S. Tri-modal person re-identification with RGB, depth and thermal features. In: Proceedings of 2013 IEEE Conference on Computer Vision and Pattern Recognition Workshops. 2013, 301\u2013307","DOI":"10.1109\/CVPRW.2013.52"},{"key":"2050_CR33","doi-asserted-by":"publisher","first-page":"697","DOI":"10.1016\/j.scs.2018.01.053","volume":"38","author":"B N Silva","year":"2018","unstructured":"Silva B N, Khan M, Han K. Towards sustainable smart cities: a review of trends, architectures, components, and open challenges in smart cities. Sustainable Cities and Society, 2018, 38: 697\u2013713","journal-title":"Sustainable Cities and Society"},{"key":"2050_CR34","doi-asserted-by":"publisher","first-page":"103007","DOI":"10.1016\/j.jnca.2021.103007","volume":"181","author":"U Majeed","year":"2021","unstructured":"Majeed U, Khan L U, Yaqoob I, Kazmi S M A, Salah K, Hong C S. Blockchain for IoT-based smart cities: recent advances, requirements, and future challenges. Journal of Network and Computer Applications, 2021, 181: 103007","journal-title":"Journal of Network and Computer Applications"},{"key":"2050_CR35","doi-asserted-by":"publisher","first-page":"101091","DOI":"10.1016\/j.eti.2020.101091","volume":"20","author":"F Ullah","year":"2020","unstructured":"Ullah F, Al-Turjman F, Nayyar A. IoT-based green city architecture using secured and sustainable android services. Environmental Technology & Innovation, 2020, 20: 101091","journal-title":"Environmental Technology & Innovation"},{"key":"2050_CR36","doi-asserted-by":"publisher","first-page":"42458","DOI":"10.1109\/ACCESS.2020.2977346","volume":"8","author":"J Li","year":"2020","unstructured":"Li J, Wang J, Ullah F. An end-to-end task-simplified and anchor-guided deep learning framework for image-based head pose estimation. IEEE Access, 2020, 8: 42458\u201342468","journal-title":"IEEE Access"},{"issue":"1","key":"2050_CR37","doi-asserted-by":"publisher","first-page":"215","DOI":"10.1113\/jphysiol.1968.sp008455","volume":"195","author":"D H Hubel","year":"1968","unstructured":"Hubel D H, Wiesel T N. Receptive fields and functional architecture of monkey striate cortex. The Journal of Physiology, 1968, 195(1): 215\u2013243","journal-title":"The Journal of Physiology"},{"key":"2050_CR38","doi-asserted-by":"publisher","first-page":"6465","DOI":"10.1109\/ACCESS.2020.3047266","volume":"9","author":"M Bukhari","year":"2021","unstructured":"Bukhari M, Bajwa K B, Gillani S, Maqsood M, Durrani M Y, Mehmood I, Ugail H, Rho S. An efficient gait recognition method for known and unknown covariate conditions. IEEE Access, 2021, 9: 6465\u20136477","journal-title":"IEEE Access"},{"key":"2050_CR39","doi-asserted-by":"publisher","first-page":"147858","DOI":"10.1109\/ACCESS.2020.3014701","volume":"8","author":"R Ashraf","year":"2020","unstructured":"Ashraf R, Afzal S, Rehman A U, Gul S, Baber J, Bakhtyar M, Mehmood I, Song O Y, Maqsood M. Region-of-interest based transfer learning assisted framework for skin cancer detection. IEEE Access, 2020, 8: 147858\u2013147871","journal-title":"IEEE Access"},{"issue":"10","key":"2050_CR40","doi-asserted-by":"publisher","first-page":"1133","DOI":"10.3390\/math9101133","volume":"9","author":"M Maqsood","year":"2021","unstructured":"Maqsood M, Bukhari M, Ali Z, Gillani S, Mehmood I, Rho S, Jung Y-A. A residual-learning-based multi-scale parallel-convolutions- assisted efficient CAD system for liver tumor detection. Mathematics, 2021, 9(10): 1133","journal-title":"Mathematics"},{"issue":"13","key":"2050_CR41","doi-asserted-by":"publisher","first-page":"1457","DOI":"10.3390\/math9131457","volume":"9","author":"M Maqsood","year":"2021","unstructured":"Maqsood M, Yasmin S, Mehmood I, Bukhari M, Kim M. An efficient DA-net architecture for lung nodule segmentation. Mathematics, 2021, 9(13): 1457","journal-title":"Mathematics"},{"key":"2050_CR42","doi-asserted-by":"publisher","first-page":"48","DOI":"10.1016\/j.neucom.2021.03.091","volume":"452","author":"Z Niu","year":"2021","unstructured":"Niu Z, Zhong G, Yu H. A review on the attention mechanism of deep learning. Neurocomputing, 2021, 452: 48\u201362","journal-title":"Neurocomputing"},{"issue":"3","key":"2050_CR43","doi-asserted-by":"publisher","first-page":"331","DOI":"10.1007\/s41095-022-0271-y","volume":"8","author":"M H Guo","year":"2022","unstructured":"Guo M H, Xu T X, Liu J J, Liu Z N, Jiang P T, Mu T J, Zhang S H, Martin R R, Cheng M M, Hu S M. Attention mechanisms in computer vision: a survey. Computational Visual Media, 2022, 8(3): 331\u2013368","journal-title":"Computational Visual Media"},{"issue":"9","key":"2050_CR44","doi-asserted-by":"publisher","first-page":"1904","DOI":"10.1109\/TPAMI.2015.2389824","volume":"37","author":"K He","year":"2015","unstructured":"He K, Zhang X, Ren S, Sun J. Spatial pyramid pooling in deep convolutional networks for visual recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015, 37(9): 1904\u20131916","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"2050_CR45","doi-asserted-by":"crossref","unstructured":"Lazebnik S, Schmid C, Ponce J. Beyond bags of features: spatial pyramid matching for recognizing natural scene categories. In: Proceedings of 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. 2006, 2169\u20132178","DOI":"10.1109\/CVPR.2006.68"},{"key":"2050_CR46","doi-asserted-by":"crossref","unstructured":"Li W, Wang X. Locally aligned feature transforms across views. In: Proceedings of 2013 IEEE Conference on Computer Vision and Pattern Recognition. 2013, 3594\u20133601","DOI":"10.1109\/CVPR.2013.461"},{"key":"2050_CR47","doi-asserted-by":"crossref","unstructured":"Li W, Zhao R, Wang X. Human reidentification with transferred metric learning. In: Proceedings of the 11th Asian Conference on Computer Vision. 2012, 31\u201344","DOI":"10.1007\/978-3-642-37331-2_3"},{"key":"2050_CR48","doi-asserted-by":"crossref","unstructured":"K\u00f6stinger M, Hirzer M, Wohlhart P, Roth P M, Bischof H. Large scale metric learning from equivalence constraints. In: Proceedings of 2012 IEEE Conference on Computer Vision and Pattern Recognition. 2012, 2288\u20132295","DOI":"10.1109\/CVPR.2012.6247939"},{"key":"2050_CR49","doi-asserted-by":"crossref","unstructured":"Zheng L, Shen L, Tian L, Wang S, Wang J, Tian Q. Scalable person re-identification: a benchmark. In: Proceedings of 2015 IEEE International Conference on Computer Vision. 2015, 1116\u20131124","DOI":"10.1109\/ICCV.2015.133"},{"issue":"4","key":"2050_CR50","first-page":"83","volume":"14","author":"H Fan","year":"2018","unstructured":"Fan H, Zheng L, Yan C, Yang Y. Unsupervised person re-identification: clustering and fine-tuning. ACM Transactions on Multimedia Computing, Communications, and Applications, 2018, 14(4): 83","journal-title":"Communications, and Applications"},{"issue":"3","key":"2050_CR51","doi-asserted-by":"publisher","first-page":"2087","DOI":"10.1007\/s11063-019-10000-4","volume":"50","author":"G Feng","year":"2019","unstructured":"Feng G, Liu W, Tao D, Zhou Y. Hessian regularized distance metric learning for people re-identification. Neural Processing Letters, 2019, 50(3): 2087\u20132100","journal-title":"Neural Processing Letters"}],"container-title":["Frontiers of Computer Science"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11704-022-2050-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11704-022-2050-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11704-022-2050-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,18]],"date-time":"2024-09-18T20:26:09Z","timestamp":1726691169000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11704-022-2050-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,12,12]]},"references-count":51,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2023,8]]}},"alternative-id":["2050"],"URL":"https:\/\/doi.org\/10.1007\/s11704-022-2050-4","relation":{},"ISSN":["2095-2228","2095-2236"],"issn-type":[{"value":"2095-2228","type":"print"},{"value":"2095-2236","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,12,12]]},"assertion":[{"value":"25 January 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 July 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 December 2022","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}],"article-number":"174329"}}