{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,2]],"date-time":"2026-04-02T09:39:38Z","timestamp":1775122778101,"version":"3.50.1"},"reference-count":40,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2021,4,15]],"date-time":"2021-04-15T00:00:00Z","timestamp":1618444800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2021,4,15]],"date-time":"2021-04-15T00:00:00Z","timestamp":1618444800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Machine Vision and Applications"],"published-print":{"date-parts":[[2021,5]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Maritime vessel re-identification (re-ID) is a computer vision task of vessel identity matching across disjoint camera views. Prominent applications of vessel re-ID exist in the fields of surveillance and maritime traffic flow analysis. However, the field suffers from the absence of a large-scale dataset that enables training of deep learning models. In this study, we present a new dataset that includes 4614\u00a0images of 729\u00a0vessels along with 5-bin orientation and 8-class vessel-type annotations to promote further research. A second contribution of this study is the baseline re-ID analysis of our new dataset. Performances of 10 recent deep learning architectures are quantitatively compared to reveal the best practices. Lastly, we propose a novel multi-branch deep learning architecture, Maritime Vessel Re-ID network (MVR-net), to address the challenging problem of vessel re-ID. Evaluation of our approach on the new dataset yields 74.5% mAP and 77.9% Rank-1 score, providing a performance increase of 5.7% mAP and 5.0% Rank-1 over the best-performing baseline. MVR-net also outperforms the PRN (a pioneering vehicle re-ID network), by 2.9% and 4.3% higher mAP and Rank-1, respectively.<\/jats:p>","DOI":"10.1007\/s00138-021-01199-1","type":"journal-article","created":{"date-parts":[[2021,4,15]],"date-time":"2021-04-15T22:02:17Z","timestamp":1618524137000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":24,"title":["Maritime vessel re-identification: novel VR-VCA dataset and a multi-branch architecture MVR-net"],"prefix":"10.1007","volume":"32","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3572-5496","authenticated-orcid":false,"given":"Amir","family":"Ghahremani","sequence":"first","affiliation":[]},{"given":"Tunc","family":"Alkanat","sequence":"additional","affiliation":[]},{"given":"Egor","family":"Bondarev","sequence":"additional","affiliation":[]},{"given":"Peter H. N.","family":"de With","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,4,15]]},"reference":[{"key":"1199_CR1","doi-asserted-by":"publisher","first-page":"43","DOI":"10.1016\/j.drugpo.2016.07.010","volume":"39","author":"MP Atkinson","year":"2017","unstructured":"Atkinson, M.P., Kress, M., Szechtman, R.: Maritime transportation of illegal drugs from south america. Int. J. Drug Policy 39, 43\u201351 (2017)","journal-title":"Int. J. Drug Policy"},{"issue":"2","key":"1199_CR2","doi-asserted-by":"publisher","first-page":"130","DOI":"10.1016\/j.cviu.2012.10.008","volume":"117","author":"L Bazzani","year":"2013","unstructured":"Bazzani, L., Cristani, M., Murino, V.: Symmetry-driven accumulation of local features for human characterization and re-identification. Comput. Vis. Image Underst. 117(2), 130\u2013144 (2013)","journal-title":"Comput. Vis. Image Underst."},{"issue":"4","key":"1199_CR3","doi-asserted-by":"publisher","first-page":"270","DOI":"10.1016\/j.imavis.2014.02.001","volume":"32","author":"A Bedagkar-Gala","year":"2014","unstructured":"Bedagkar-Gala, A., Shah, S.K.: A survey of approaches and trends in person re-identification. Image Vis. Comput. 32(4), 270\u2013286 (2014)","journal-title":"Image Vis. Comput."},{"key":"1199_CR4","unstructured":"Chen, H., Lagadec, B., Bremond, F.: Partition and reunion: a two-branch neural network for vehicle re-identification. In: Proceedings of the CVPR Workshops, pp. 184\u2013192 (2019)"},{"key":"1199_CR5","doi-asserted-by":"crossref","unstructured":"Chen, W., Chen, X., Zhang, J., Huang, K.: Beyond triplet loss: a deep quadruplet network for person re-identification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 403\u2013412 (2017)","DOI":"10.1109\/CVPR.2017.145"},{"key":"1199_CR6","unstructured":"Corvee, E., Bremond, F., Thonnat, M., et\u00a0al.: Person re-identification using spatial covariance regions of human body parts. In: 2010 7th IEEE International Conference on Advanced Video and Signal Based Surveillance, pp. 435\u2013440. IEEE (2010)"},{"key":"1199_CR7","doi-asserted-by":"publisher","first-page":"114","DOI":"10.1016\/j.actaastro.2012.06.009","volume":"80","author":"E Detsis","year":"2012","unstructured":"Detsis, E., Brodsky, Y., Knudtson, P., Cuba, M., Fuqua, H., Szalai, B.: Project catch: a space based solution to combat illegal, unreported and unregulated fishing: Part i: vessel monitoring system. Acta Astron. 80, 114\u2013123 (2012)","journal-title":"Acta Astron."},{"key":"1199_CR8","doi-asserted-by":"crossref","unstructured":"Ghahremani, A., Bondarev, E., de\u00a0With, P.H.: Toward robust multitype and orientation detection of vessels in maritime surveillance. Electronic Imaging (2020)","DOI":"10.1117\/1.JEI.29.3.033015"},{"key":"1199_CR9","doi-asserted-by":"crossref","unstructured":"Ghahremani, A., Kong, Y., Bondarev, E., et\u00a0al.: Towards parameter-optimized vessel re-identification based on iornet. In: International Conference on Computational Science, pp. 125\u2013136. Springer (2019)","DOI":"10.1007\/978-3-030-22750-0_10"},{"key":"1199_CR10","doi-asserted-by":"crossref","unstructured":"Gheissari, N., Sebastian, T.B., Hartley, R.: Person reidentification using spatiotemporal appearance. In: 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR\u201906), vol.\u00a02, pp. 1528\u20131535. IEEE (2006)","DOI":"10.1109\/CVPR.2006.223"},{"key":"1199_CR11","doi-asserted-by":"crossref","unstructured":"Groot, H.G., Zwemer, M.H., Wijnhoven, R., Bondarau, E., et\u00a0al.: Vessel-speed enforcement system by multi-camera detection and re-identification. In: 15th International Conference on Computer Vision Theory and Applications 2020 (2020)","DOI":"10.5220\/0008911202680277"},{"key":"1199_CR12","unstructured":"Hamdoun, O., Moutarde, F., Stanciulescu, B., Steux, B.: Interest points harvesting in video sequences for efficient person identification. In: The Eighth International Workshop on Visual Surveillance-VS2008 (2008)"},{"key":"1199_CR13","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"1199_CR14","unstructured":"Hermans, A., Beyer, L., Leibe, B.: In defense of the triplet loss for person re-identification. arXiv preprint arXiv:1703.07737 (2017)"},{"key":"1199_CR15","doi-asserted-by":"crossref","unstructured":"Hirzer, M., Roth, P.M., Bischof, H.: Person re-identification by efficient impostor-based metric learning. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-Based Surveillance, pp. 203\u2013208. IEEE (2012)","DOI":"10.1109\/AVSS.2012.55"},{"key":"1199_CR16","doi-asserted-by":"crossref","unstructured":"J\u00fcngling, K., Bodensteiner, C., Arens, M.: Person re-identification in multi-camera networks. In: CVPR 2011 WORKSHOPS, pp. 55\u201361. IEEE (2011)","DOI":"10.1109\/CVPRW.2011.5981771"},{"key":"1199_CR17","doi-asserted-by":"crossref","unstructured":"Kalayeh, M.M., Basaran, E., G\u00f6kmen, M., Kamasak, M.E., Shah, M.: Human semantic parsing for person re-identification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1062\u20131071 (2018)","DOI":"10.1109\/CVPR.2018.00117"},{"key":"1199_CR18","doi-asserted-by":"publisher","first-page":"50","DOI":"10.1016\/j.cviu.2019.03.001","volume":"182","author":"SD Khan","year":"2019","unstructured":"Khan, S.D., Ullah, H.: A survey of advances in vision-based vehicle re-identification. Comput. Vis. Image Underst. 182, 50\u201363 (2019)","journal-title":"Comput. Vis. Image Underst."},{"key":"1199_CR19","unstructured":"Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)"},{"key":"1199_CR20","doi-asserted-by":"crossref","unstructured":"Koestinger, M., Hirzer, M., Wohlhart, P., Roth, P.M., Bischof, H.: Large scale metric learning from equivalence constraints. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition, pp. 2288\u20132295. IEEE (2012)","DOI":"10.1109\/CVPR.2012.6247939"},{"key":"1199_CR21","doi-asserted-by":"crossref","unstructured":"Kuma, R., Weill, E., Aghdasi, F., Sriram, P.: Vehicle re-identification: an efficient baseline using triplet embedding. In: 2019 International Joint Conference on Neural Networks (IJCNN), pp. 1\u20139. IEEE (2019)","DOI":"10.1109\/IJCNN.2019.8852059"},{"key":"1199_CR22","doi-asserted-by":"crossref","unstructured":"Li, W., Zhu, X., Gong, S.: Harmonious attention network for person re-identification. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 2285\u20132294 (2018)","DOI":"10.1109\/CVPR.2018.00243"},{"key":"1199_CR23","doi-asserted-by":"crossref","unstructured":"Liao, S., Hu, Y., Zhu, X., Li, S.Z.: Person re-identification by local maximal occurrence representation and metric learning. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2197\u20132206 (2015)","DOI":"10.1109\/CVPR.2015.7298832"},{"key":"1199_CR24","doi-asserted-by":"crossref","unstructured":"Liu, X., Zhang, S., Huang, Q., Gao, W.: Ram: a region-aware deep model for vehicle re-identification. In: 2018 IEEE International Conference on Multimedia and Expo (ICME), pp. 1\u20136. IEEE (2018)","DOI":"10.1109\/ICME.2018.8486589"},{"key":"1199_CR25","doi-asserted-by":"crossref","unstructured":"Ma, B., Su, Y., Jurie, F.: Local descriptors encoded by fisher vectors for person re-identification. In: European Conference on Computer Vision, pp. 413\u2013422. Springer (2012)","DOI":"10.1007\/978-3-642-33863-2_41"},{"key":"1199_CR26","doi-asserted-by":"crossref","unstructured":"McLaughlin, N., Martinez\u00a0del Rincon, J., Miller, P.: Recurrent convolutional network for video-based person re-identification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1325\u20131334 (2016)","DOI":"10.1109\/CVPR.2016.148"},{"issue":"2","key":"1199_CR27","first-page":"65","volume":"4","author":"UJ Orji","year":"2013","unstructured":"Orji, U.J., et al.: Tackling piracy and other illegal activities in nigerian waters. JoDRM 4(2), 65\u201370 (2013)","journal-title":"JoDRM"},{"key":"1199_CR28","first-page":"6","volume":"2","author":"BJ Prosser","year":"2010","unstructured":"Prosser, B.J., Zheng, W.S., Gong, S., Xiang, T., Mary, Q.: Person re-identification by support vector ranking. BMVC 2, 6 (2010)","journal-title":"BMVC"},{"key":"1199_CR29","doi-asserted-by":"publisher","first-page":"27744","DOI":"10.1109\/ACCESS.2020.2969231","volume":"8","author":"D Qiao","year":"2020","unstructured":"Qiao, D., Liu, G., Dong, F., Jiang, S.X., Dai, L.: Marine vessel re-identification: a large-scale dataset and global-and-local fusion-based discriminative feature learning. IEEE Access 8, 27744\u201327756 (2020)","journal-title":"IEEE Access"},{"key":"1199_CR30","doi-asserted-by":"publisher","first-page":"112","DOI":"10.1016\/j.envdev.2013.08.002","volume":"11","author":"K Sander","year":"2014","unstructured":"Sander, K., Lee, J., Hickey, V., Mosoti, V.B., Virdin, J., Magrath, W.B.: Conceptualizing maritime environmental and natural resources law enforcement-the case of illegal fishing. Environ. Dev. 11, 112\u2013122 (2014)","journal-title":"Environ. Dev."},{"key":"1199_CR31","doi-asserted-by":"crossref","unstructured":"Su, C., Li, J., Zhang, S., Xing, J., Gao, W., Tian, Q.: Pose-driven deep convolutional model for person re-identification. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3960\u20133969 (2017)","DOI":"10.1109\/ICCV.2017.427"},{"key":"1199_CR32","doi-asserted-by":"crossref","unstructured":"Wang, G., Yuan, Y., Chen, X., Li, J., Zhou, X.: Learning discriminative features with multiple granularities for person re-identification. In: Proceedings of the 26th ACM International Conference on Multimedia, pp. 274\u2013282 (2018)","DOI":"10.1145\/3240508.3240552"},{"key":"1199_CR33","doi-asserted-by":"crossref","unstructured":"Wang, Z., Tang, L., Liu, X., Yao, Z., Yi, S., Shao, J., Yan, J., Wang, S., Li, H., Wang, X.: Orientation invariant feature embedding and spatial temporal regularization for vehicle re-identification. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 379\u2013387 (2017)","DOI":"10.1109\/ICCV.2017.49"},{"key":"1199_CR34","unstructured":"Zajdel, W., Zivkovic, Z., Krose, B.: Keeping track of humans: Have i seen this person before? In: Proceedings of the 2005 IEEE International Conference on Robotics and Automation, pp. 2081\u20132086. IEEE (2005)"},{"key":"1199_CR35","doi-asserted-by":"crossref","unstructured":"Zapletal, D., Herout, A.: Vehicle re-identification for automatic video traffic surveillance. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 25\u201331 (2016)","DOI":"10.1109\/CVPRW.2016.195"},{"key":"1199_CR36","unstructured":"Zheng, L., Yang, Y., Hauptmann, A.G.: Person re-identification: Past, present and future. arXiv preprint arXiv:1610.02984 (2016)"},{"key":"1199_CR37","doi-asserted-by":"crossref","unstructured":"Zheng, W.S., Gong, S., Xiang, T.: Person re-identification by probabilistic relative distance comparison. In: CVPR 2011, pp. 649\u2013656. IEEE (2011)","DOI":"10.1109\/CVPR.2011.5995598"},{"key":"1199_CR38","doi-asserted-by":"crossref","unstructured":"Zheng, Z., Zheng, L., Yang, Y.: Unlabeled samples generated by gan improve the person re-identification baseline in vitro. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3754\u20133762 (2017)","DOI":"10.1109\/ICCV.2017.405"},{"key":"1199_CR39","doi-asserted-by":"crossref","unstructured":"Zhong, Z., Zheng, L., Cao, D., Li, S.: Re-ranking person re-identification with k-reciprocal encoding. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1318\u20131327 (2017)","DOI":"10.1109\/CVPR.2017.389"},{"key":"1199_CR40","unstructured":"Zhong, Z., Zheng, L., Kang, G., Li, S., Yang, Y.: Random erasing data augmentation. arXiv preprint arXiv:1708.04896 (2017)"}],"container-title":["Machine Vision and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00138-021-01199-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00138-021-01199-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00138-021-01199-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,28]],"date-time":"2024-08-28T13:09:40Z","timestamp":1724850580000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00138-021-01199-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,4,15]]},"references-count":40,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2021,5]]}},"alternative-id":["1199"],"URL":"https:\/\/doi.org\/10.1007\/s00138-021-01199-1","relation":{},"ISSN":["0932-8092","1432-1769"],"issn-type":[{"value":"0932-8092","type":"print"},{"value":"1432-1769","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,4,15]]},"assertion":[{"value":"17 July 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 January 2021","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 March 2021","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 April 2021","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"71"}}