{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,19]],"date-time":"2026-05-19T23:33:29Z","timestamp":1779233609825,"version":"3.51.4"},"reference-count":36,"publisher":"MDPI AG","issue":"14","license":[{"start":{"date-parts":[[2021,7,7]],"date-time":"2021-07-07T00:00:00Z","timestamp":1625616000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>This paper presents a camera-based vessel-speed enforcement system based on two cameras. The proposed system detects and tracks vessels per camera view and employs a re-identification (re-ID) function for linking vessels between the two cameras based on multiple bounding-box images per vessel. Newly detected vessels in one camera (query) are compared to the gallery set of all vessels detected by the other camera. To train and evaluate the proposed detection and re-ID system, a new Vessel-reID dataset is introduced. This extensive dataset has captured a total of 2474 different vessels covered in multiple images, resulting in a total of 136,888 vessel bounding-box images. Multiple CNN detector architectures are evaluated in-depth. The SSD512 detector performs best with respect to its speed (85.0% Recall@95Precision at 20.1 frames per second). For the re-ID of vessels, a large portion of the total trajectory can be covered by the successful detections of the SSD model. The re-ID experiments start with a baseline single-image evaluation obtaining a score of 55.9% Rank-1 (49.7% mAP) for the existing TriNet network, while the available MGN model obtains 68.9% Rank-1 (62.6% mAP). The performance significantly increases with 5.6% Rank-1 (5.7% mAP) for MGN by applying matching with multiple images from a single vessel. When emphasizing more fine details by selecting only the largest bounding-box images, another 2.0% Rank-1 (1.4% mAP) is added. Application-specific optimizations such as travel-time selection and applying a cross-camera matching constraint further enhance the results, leading to a final 88.9% Rank-1 and 83.5% mAP performance.<\/jats:p>","DOI":"10.3390\/s21144659","type":"journal-article","created":{"date-parts":[[2021,7,7]],"date-time":"2021-07-07T12:31:25Z","timestamp":1625661085000},"page":"4659","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Multi-Camera Vessel-Speed Enforcement by Enhancing Detection and Re-Identification Techniques"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0835-202X","authenticated-orcid":false,"given":"Matthijs H.","family":"Zwemer","sequence":"first","affiliation":[{"name":"Department of Electrical Engineering, Eindhoven University of Technology, 5600 MB Eindhoven, The Netherlands"},{"name":"ViNotion B.V., 5641 JA Eindhoven, The Netherlands"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6047-9143","authenticated-orcid":false,"given":"Herman G. J.","family":"Groot","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, Eindhoven University of Technology, 5600 MB Eindhoven, The Netherlands"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8837-3976","authenticated-orcid":false,"given":"Rob","family":"Wijnhoven","sequence":"additional","affiliation":[{"name":"ViNotion B.V., 5641 JA Eindhoven, The Netherlands"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Egor","family":"Bondarev","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, Eindhoven University of Technology, 5600 MB Eindhoven, The Netherlands"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Peter H. N.","family":"de With","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, Eindhoven University of Technology, 5600 MB Eindhoven, The Netherlands"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,7,7]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.rse.2017.12.033","article-title":"Vessel detection and classification from spaceborne optical images: A literature survey","volume":"207","author":"Kanjir","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Groot, H.G.J., Zwemer, M.H., Wijnhoven, R.G.J., Bondarev, Y., and de With, P.H.N. (2020, January 27\u201329). Vessel-speed Enforcement System by Multi-camera Detection and Re-identification. Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, Valetta, Malta.","DOI":"10.5220\/0008911202680277"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1477","DOI":"10.1142\/S0218001409007594","article-title":"Argos\u2014A video surveillance system for boat traffic monitoring in Venice","volume":"23","author":"Bloisi","year":"2009","journal-title":"Int. J. Pattern Recognit. Artif. Intell."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Qiao, D., Liu, G., Zhang, J., Zhang, Q., Wu, G., and Dong, F. (2019). M3C: Multimodel-and-Multicue-Based Tracking by Detection of Surrounding Vessels in Maritime Environment for USV. Electronics, 8.","DOI":"10.3390\/electronics8070723"},{"key":"ref_5","unstructured":"Redmon, J., and Farhadi, A. (2018). YOLOv3: An Incremental Improvement. arXiv."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s00138-021-01199-1","article-title":"Maritime vessel re-identification: Novel VR-VCA dataset and a multi-branch architecture MVR-net","volume":"32","author":"Ghahremani","year":"2021","journal-title":"Mach. Vis. Appl."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Girshick, R., Donahue, J., Darrell, T., and Malik, J. (2014, January 23\u201328). Rich feature hierarchies for accurate object detection and semantic segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2014, Columbus, OH, USA.","DOI":"10.1109\/CVPR.2014.81"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Girshick, R. (2015, January 7\u201313). Fast R-CNN. Proceedings of the International Conference on Computer Vision 2015, Santiago, Chile.","DOI":"10.1109\/ICCV.2015.169"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1137","DOI":"10.1109\/TPAMI.2016.2577031","article-title":"Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks","volume":"39","author":"Ren","year":"2016","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"He, K., Gkioxari, G., Doll\u00e1r, P., and Girshick, R. (2017, January 22\u201329). Mask R-CNN. Proceedings of the International Conference on Computer Vision 2017, Venice, Italy.","DOI":"10.1109\/ICCV.2017.322"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Redmon, J., Divvala, S., Girshick, R., and Farhadi, A. (2016, January 27\u201330). You Only Look Once: Unified, Real-Time Object Detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2016, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.91"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Redmon, J., and Farhadi, A. (2017, January 21\u201326). YOLO9000: Better, Faster, Stronger. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2017, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.690"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.Y., and Berg, A.C. (2016). SSD: Single shot multibox detector. European Conference on Computer Vision, Springer.","DOI":"10.1007\/978-3-319-46448-0_2"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Tian, Z., Shen, C., Chen, H., and He, T. (2019, January 27\u201328). FCOS: Fully Convolutional One-Stage Object Detection. Proceedings of the International Conference on Computer Vision 2019, Seoul, Korea.","DOI":"10.1109\/ICCV.2019.00972"},{"key":"ref_15","unstructured":"Wijnhoven, R., van Rens, K., Jaspers, E., and de With, P. (2010, January 11\u201312). Online learning for ship detection in maritime surveillance. Proceedings of the 31th Symposium on Information Theory in the Benelux, Rotterdam, The Netherlands."},{"key":"ref_16","first-page":"87","article-title":"Ship detection in port surveillance based on context and motion saliency analysis","volume":"Volume 8663","author":"Loce","year":"2013","journal-title":"Video Surveillance and Transportation Imaging Applications"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Zwemer, M.H., Wijnhoven, R.G., and de With, P.H.N. (2018, January 27\u201329). Ship Detection in Harbour Surveillance based on Large-Scale Data and CNNs. Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, Funchal, Madeira, Portugal.","DOI":"10.5220\/0006541501530160"},{"key":"ref_18","unstructured":"Chen, H., Lagadec, B., and Bremond, F. (2019, January 16\u201317). Partition and Reunion: A Two-Branch Neural Network for Vehicle Re-identification. Proceedings of the 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Long Beach, CA, USA."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"523","DOI":"10.1109\/TPAMI.2018.2807450","article-title":"A Systematic Evaluation and Benchmark for Person Re-Identification: Features, Metrics, and Datasets","volume":"41","author":"Karanam","year":"2019","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Ahmed, E., Jones, M., and Marks, T.K. (2015, January 7\u201312). An improved deep learning architecture for person re-identification. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2015, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7299016"},{"key":"ref_21","unstructured":"Hermans, A., Beyer, L., and Leibe, B. (2017). In Defense of the Triplet Loss for Person Re-Identification. arXiv."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Wang, G., Yuan, Y., Chen, X., Li, J., and Zhou, X. (2018, January 22\u201326). Learning discriminative features with multiple granularities for person re-identification. Proceedings of the 2018 ACM Multimedia Conference on Multimedia Conference, ACM, 2015, Seoul, Korea.","DOI":"10.1145\/3240508.3240552"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Luo, H., Gu, Y., Liao, X., Lai, S., and Jiang, W. (2019, January 16\u201317). Bag of Tricks and a Strong Baseline for Deep Person Re-Identification. Proceedings of the 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Long Beach, CA, USA.","DOI":"10.1109\/CVPRW.2019.00190"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Zheng, F., Deng, C., Sun, X., Jiang, X., Guo, X., Yu, Z., Huang, F., and Ji, R. (2019, January 16\u201317). Pyramidal Person Re-IDentification via Multi-Loss Dynamic Training. Proceedings of the 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00871"},{"key":"ref_25","unstructured":"Liu, X., Groot, H.G.J., Bondarau, E., and de With, P.H.N. (2020, January 26\u201330). Introducing Scene Understanding to Person Re-Identification using a Spatio-Temporal Multi-Camera Model. Proceedings of the IS&T International Symposium on Electronic Imaging 2020, Image Processing: Algorithms and Systems XVIII, Burlingame, CA, USA."},{"key":"ref_26","unstructured":"Shi, J., and Tomasi, C. (1994, January 21\u201323). Good features to track. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 1994, Seattle, WA, USA."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Zheng, Z., Zheng, L., and Yang, Y. (2017, January 21\u201326). Unlabeled samples generated by gan improve the person re-identification baseline in vitro. Proceedings of the IEEE International Conference on Computer Vision 2017, Venice, Italy.","DOI":"10.1109\/ICCV.2017.405"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Zheng, L., Shen, L., Tian, L., Wang, S., Wang, J., and Tian, Q. (2015, January 7\u201313). Scalable person re-identification: A benchmark. Proceedings of the IEEE International Conference on Computer Vision 2015, Santiago, Chile.","DOI":"10.1109\/ICCV.2015.133"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Doll\u00e1r, P., and Zitnick, C.L. (2014, January 6\u201312). Microsoft coco: Common objects in context. Proceedings of the European Conference on Computer Vision, Zurich, Switzerland.","DOI":"10.1007\/978-3-319-10602-1_48"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1007\/s11263-015-0816-y","article-title":"ImageNet Large Scale Visual Recognition Challenge","volume":"115","author":"Russakovsky","year":"2015","journal-title":"Int. J. Comput. Vis. IJCV"},{"key":"ref_31","unstructured":"Everingham, M., Van Gool, L., Williams, C.K.I., Winn, J., and Zisserman, A. (2012, January 7\u201313). The PASCAL Visual Object Classes Challenge 2012 (VOC2012) Results. Proceedings of the European Conference on Computer Vision Workshops 2012, Firenze, Italy. Available online: http:\/\/host.robots.ox.ac.uk\/pascal\/VOC\/voc2012\/."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1186\/s41074-017-0033-4","article-title":"Generic and attribute-specific deep representations for maritime vessels","volume":"9","author":"Solmaz","year":"2017","journal-title":"IPSJ Trans. Comput. Vis. Appl."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"266-1","DOI":"10.2352\/ISSN.2470-1173.2019.11.IPAS-266","article-title":"Multi-Class detection and orientation recognition of vessels in maritime surveillance","volume":"2019","author":"Ghahremani","year":"2019","journal-title":"Electron. Imaging"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"2593","DOI":"10.1109\/TMM.2018.2865686","article-title":"SeaShips: A Large-Scale Precisely Annotated Dataset for Ship Detection","volume":"20","author":"Shao","year":"2018","journal-title":"IEEE Trans. Multimed."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Manning, C.D., Raghavan, P., and Sch\u00fctze, H. (2008). Evaluation in information retrieval. Introduction to Information Retrieval, Cambridge University Press.","DOI":"10.1017\/CBO9780511809071"},{"key":"ref_36","unstructured":"Groot, H.G.J., Bondarau, E., and de With, P.H.N. (2019, January 26\u201330). Improving Person Re-Identification Performance by Customized Dataset and Person Detection. Proceedings of the IS&T International Symposium on Electronic Imaging 2019, Image Processing: Algorithms and Systems XVII, Burlingame, CA, USA."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/14\/4659\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T06:27:18Z","timestamp":1760164038000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/14\/4659"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,7,7]]},"references-count":36,"journal-issue":{"issue":"14","published-online":{"date-parts":[[2021,7]]}},"alternative-id":["s21144659"],"URL":"https:\/\/doi.org\/10.3390\/s21144659","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,7,7]]}}}