{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,8]],"date-time":"2026-09-08T17:09:26Z","timestamp":1788887366417,"version":"build-2803163510"},"reference-count":43,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2026,9,8]],"date-time":"2026-09-08T00:00:00Z","timestamp":1788825600000},"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>Multi-frame fusion is standard for converting frame-by-frame license plate character detections into stable, reliable readings. Character Time-Series Matching (CTM), a leading approach, associates characters across frames using the Hungarian algorithm with a fixed Euclidean distance threshold and a translation-only motion model, reporting 96.7% accuracy on the UFPR-ALPR dataset. In this work, we demonstrate that this high performance is protocol-dependent: when ground-truth static plate crops and pre-segmented tracks are used, CTM performs strongly. However, in real-world scenarios involving moving cameras (such as drone-mounted cameras, helmet-mounted cameras, and mobile platforms) where inter-frame geometry changes dynamically, baseline multi-frame association frameworks that combine fixed spatial gates with unconstrained translation propagation fail. In these cases, temporal fusion provides no benefit and degrades plate recognition performance below the single-frame baseline. Indeed, under its own Intersection over Union (IoU) tracker, this literature method correctly reads only 15.1% of plates in traffic videos recorded with a real moving camera (86 human-verified tracks). To address this vulnerability, we propose Geo-CTM (Geometry-Constrained CTM), an association pipeline integrating height-scaled adaptive matching gates, inter-frame similarity estimation via Random Sample Consensus (RANSAC), transform-guided character coasting, and co-occurrence-constrained duplicate track elimination. Systematic motion-model ablation demonstrates that while the complete association pipeline provides the primary foundation for robustness (raising mean accuracy from 85.40% to over 91.5%), estimating a similarity transform (91.82%) delivers the most physically grounded and identifiable representation on planar plates without estimation degeneration. While our method performs comparably to CTM on ideal data when using the same detector and detections, it minimizes performance loss under geometric distortion conditions where CTM is inadequate. For instance, a statistically significant improvement is achieved under a 0 \u2192 60\u00b0 perspective change; in real traffic videos, with the tracker held fixed so that the fusion layer is the only variable, performance rises from 15.1% to 26.7% under the IoU tracker of the original system and from 16.3% to 29.1% under ByteTrack (+11.6 and +12.8 points; exact McNemar p=0.021 and p=0.013), whereas changing the tracker alone while holding the fusion layer fixed moves accuracy by only 1\u20132 points and is not statistically significant. Finally, our error taxonomy analysis demonstrates that on the undistorted benchmark all residual errors correspond to zero-evidence cases beyond the reach of decision-level fusion, while under dynamic perspective distortion errors are dominated by association misalignment, highlighting the specific development areas that future performance improvements must target.<\/jats:p>","DOI":"10.3390\/s26185704","type":"journal-article","created":{"date-parts":[[2026,9,8]],"date-time":"2026-09-08T16:46:04Z","timestamp":1788885964000},"page":"5704","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Geometry-Constrained Multi-Frame Character Association for License Plate Recognition on Moving Cameras"],"prefix":"10.3390","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6769-2097","authenticated-orcid":false,"given":"Ufuk","family":"Asil","sequence":"first","affiliation":[{"name":"Department of Software Engineering, OST\u0130M Technical University, Ankara 06374, T\u00fcrkiye"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"software","vocabulary":"credit"},{"role":"visualization","vocabulary":"credit"},{"role":"methodology","vocabulary":"credit"},{"role":"writing-review-editing","vocabulary":"credit"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-4263-9632","authenticated-orcid":false,"given":"\u0130lker","family":"Yoncac\u0131","sequence":"additional","affiliation":[{"name":"Department of Software Engineering, OST\u0130M Technical University, Ankara 06374, T\u00fcrkiye"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"methodology","vocabulary":"credit"},{"role":"validation","vocabulary":"credit"},{"role":"writing-review-editing","vocabulary":"credit"},{"role":"conceptualization","vocabulary":"credit"}]}],"member":"1968","published-online":{"date-parts":[[2026,9,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"11203","DOI":"10.1109\/ACCESS.2020.3047929","article-title":"Automated License Plate Recognition: A Survey on Methods and Techniques","volume":"9","author":"Shashirangana","year":"2021","journal-title":"IEEE Access"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Laroca, R., Severo, E., Zanlorensi, L.A., Oliveira, L.S., Gon\u00e7alves, G.R., Schwartz, W.R., and Menotti, D. (2018). A Robust Real-Time Automatic License Plate Recognition Based on the YOLO Detector. Proceedings of the 2018 International Joint Conference on Neural Networks (IJCNN), IEEE.","DOI":"10.1109\/IJCNN.2018.8489629"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"483","DOI":"10.1049\/itr2.12030","article-title":"An Efficient and Layout-Independent Automatic License Plate Recognition System Based on the YOLO Detector","volume":"15","author":"Laroca","year":"2021","journal-title":"IET Intell. Transp. Syst."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Silva, S.M., and Jung, C.R. (2018). License Plate Detection and Recognition in Unconstrained Scenarios. Proceedings of the European Conference on Computer Vision (ECCV), Springer.","DOI":"10.1007\/978-3-030-01258-8_36"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"4917097","DOI":"10.1155\/2024\/4917097","article-title":"Efficient Multistage License Plate Detection and Recognition Using YOLOv8 and CNN for Smart Parking Systems","volume":"2024","author":"Safran","year":"2024","journal-title":"J. Sens."},{"key":"ref_6","first-page":"139","article-title":"D-ALPR: Drone-Based Automatic License Plate Recognition Within Restricted Parking Environment","volume":"Volume 1116","author":"Maisuria","year":"2024","journal-title":"Proceedings of the Innovations in Computational Intelligence and Computer Vision (ICICV)"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Reddy, S., Mathew, M., Gomez, L., Rusinol, M., Karatzas, D., and Jawahar, C.V. (2020). RoadText-1K: Text Detection & Recognition Dataset for Driving Videos. Proceedings of the 2020 IEEE International Conference on Robotics and Automation (ICRA), IEEE.","DOI":"10.1109\/ICRA40945.2020.9196577"},{"key":"ref_8","first-page":"397","article-title":"Efficient Video Text Recognition Using Multiple Frame Integration","volume":"Volume 2","author":"Hua","year":"2002","journal-title":"Proceedings of the 2002 International Conference on Image Processing (ICIP)"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Ammar, A., Koubaa, A., Boulila, W., Benjdira, B., and Alhabashi, Y. (2023). A Multi-Stage Deep-Learning-Based Vehicle and License Plate Recognition System with Real-Time Edge Inference. Sensors, 23.","DOI":"10.3390\/s23042120"},{"key":"ref_10","unstructured":"Tran, D.T., Nguyen, T.T., John, V., Nguyen, P.L., and Kawanishi, Y. (2026). TraRA: Trajectory-Level Recognition Aggregation for Video Text Spotting in Urban Surveillance. arXiv."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Bewley, A., Ge, Z., Ott, L., Ramos, F., and Upcroft, B. (2016). Simple Online and Realtime Tracking. Proceedings of the 2016 IEEE International Conference on Image Processing (ICIP), IEEE.","DOI":"10.1109\/ICIP.2016.7533003"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Sun, P., Jiang, Y., Yu, D., Weng, F., Yuan, Z., Luo, P., Liu, W., and Wang, X. (2022). ByteTrack: Multi-Object Tracking by Associating Every Detection Box. Proceedings of the European Conference on Computer Vision (ECCV), Springer.","DOI":"10.1007\/978-3-031-20047-2_1"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Cao, J., Pang, J., Weng, X., Khirodkar, R., and Kitani, K. (2023). Observation-Centric SORT: Rethinking SORT for Robust Multi-Object Tracking. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE.","DOI":"10.1109\/CVPR52729.2023.00934"},{"key":"ref_14","unstructured":"Che, Q.H., Do Thanh, T., and Truong Van, C. (2022). Character Time-Series Matching for Robust License Plate Recognition. Proceedings of the 2022 International Conference on Multimedia Analysis and Pattern Recognition (MAPR), IEEE."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Laroca, R., Cardoso, E., Lucio, D., Estevam, V., and Menotti, D. (2022). On the Cross-Dataset Generalization in License Plate Recognition. Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISAPP), SciTePress.","DOI":"10.5220\/0010846800003124"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Xu, Z., Yang, W., Meng, A., Lu, N., Huang, H., Ying, C., and Huang, L. (2018). Towards End-to-End License Plate Detection and Recognition: A Large Dataset and Baseline. Proceedings of the European Conference on Computer Vision (ECCV), Springer.","DOI":"10.1007\/978-3-030-01261-8_16"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"189","DOI":"10.1016\/j.neucom.2021.03.103","article-title":"V-LPDR: Towards a Unified Framework for License Plate Detection, Tracking, and Recognition in Real-World Traffic Videos","volume":"449","author":"Zhang","year":"2021","journal-title":"Neurocomputing"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"4019","DOI":"10.1007\/s11263-024-02063-1","article-title":"End-to-End Video Text Spotting with Transformer","volume":"132","author":"Wu","year":"2024","journal-title":"Int. J. Comput. Vis."},{"key":"ref_19","unstructured":"Mobeen, M.M. (2026). Real-Time Automatic License Plate Recognition Using YOLOv8, SORT Tracking, and Temporal Data Interpolation. arXiv."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Ribeiro, V.N., and Hirata, N.S.T. (2024). Efficient License Plate Recognition in Videos Using Visual Rhythm and Accumulative Line Analysis. Proceedings of the Anais Estendidos da XXXVII Conference on Graphics, Patterns and Images (SIBGRAPI), SBC.","DOI":"10.5753\/sibgrapi.est.2024.31664"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"104361","DOI":"10.1016\/j.cviu.2025.104361","article-title":"MF-LPR2: Multi-Frame License Plate Image Restoration and Recognition Using Optical Flow","volume":"256","author":"Na","year":"2025","journal-title":"Comput. Vis. Image Underst."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"36203","DOI":"10.1007\/s11042-023-16910-9","article-title":"A Systematic Survey on Recent Deep Learning-Based Approaches to Multi-Object Tracking","volume":"83","author":"Agrawal","year":"2024","journal-title":"Multimed. Tools Appl."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1002\/nav.3800020109","article-title":"The Hungarian Method for the Assignment Problem","volume":"2","author":"Kuhn","year":"1955","journal-title":"Nav. Res. Logist. Q."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Wojke, N., Bewley, A., and Paulus, D. (2017). Simple Online and Realtime Tracking with a Deep Association Metric (DeepSORT). Proceedings of the 2017 IEEE International Conference on Image Processing (ICIP), IEEE.","DOI":"10.1109\/ICIP.2017.8296962"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"5693","DOI":"10.1109\/TITS.2021.3055946","article-title":"A Flexible Approach for Automatic License Plate Recognition in Unconstrained Scenarios","volume":"23","author":"Silva","year":"2022","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Yoo, H., and Jun, K. (2020). Deep Homography for License Plate Detection. Information, 11.","DOI":"10.3390\/info11040221"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"779","DOI":"10.1007\/s00530-020-00655-8","article-title":"Deep Corner Prediction to Rectify Tilted License Plate Images","volume":"27","author":"Yoo","year":"2021","journal-title":"Multimed. Syst."},{"key":"ref_28","first-page":"2017","article-title":"Spatial Transformer Networks","volume":"Volume 28","author":"Jaderberg","year":"2015","journal-title":"Proceedings of the Advances in Neural Information Processing Systems (NeurIPS)"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"381","DOI":"10.1145\/358669.358692","article-title":"Random Sample Consensus: A Paradigm for Model Fitting with Applications to Image Analysis and Automated Cartography","volume":"24","author":"Fischler","year":"1981","journal-title":"Commun. ACM"},{"key":"ref_30","unstructured":"DeTone, D., Malisiewicz, T., and Rabinovich, A. (2016). Deep Image Homography Estimation. arXiv."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"161","DOI":"10.1007\/s11263-020-01369-0","article-title":"Scene Text Detection and Recognition: The Deep Learning Era","volume":"129","author":"Long","year":"2021","journal-title":"Int. J. Comput. Vis."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"2298","DOI":"10.1109\/TPAMI.2016.2646371","article-title":"An End-to-End Trainable Neural Network for Image-Based Sequence Recognition and Its Application to Scene Text Recognition","volume":"39","author":"Shi","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_33","first-page":"13094","article-title":"TrOCR: Transformer-Based Optical Character Recognition with Pre-Trained Models","volume":"37","author":"Li","year":"2023","journal-title":"Proc. AAAI Conf. Artif. Intell. (AAAI)"},{"key":"ref_34","unstructured":"Hendrycks, D., and Dietterich, T. (2019, January 6\u20139). Benchmarking Neural Network Robustness to Common Corruptions and Perturbations. Proceedings of the International Conference on Learning Representations (ICLR), New Orleans, LA, USA."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Saha, U., Saha, B., and Imran, M.A. (2024). A Weather-Adaptive Convolutional Neural Network Framework for Better License Plate Detection. Sensors, 24.","DOI":"10.3390\/s24237841"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Atienza, R. (2021). Vision Transformer for Fast and Efficient Scene Text Recognition. Proceedings of the International Conference on Document Analysis and Recognition (ICDAR), Springer.","DOI":"10.1007\/978-3-030-86549-8_21"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Ding, J., Xue, N., Long, Y., Xia, G.S., and Lu, Q. (2019). Learning RoI Transformer for Oriented Object Detection in Aerial Images. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE.","DOI":"10.1109\/CVPR.2019.00296"},{"key":"ref_38","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":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_39","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. Proceedings of the European Conference on Computer Vision (ECCV), Springer.","DOI":"10.1007\/978-3-319-46448-0_2"},{"key":"ref_40","unstructured":"Silva, S.M., and Jung, C.R. (2017). Real-Time Brazilian License Plate Detection and Recognition Using Deep Convolutional Neural Networks. Proceedings of the 2017 30th SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI), IEEE."},{"key":"ref_41","unstructured":"Lotufo, R.A., Morgan, A.D., and Johnson, A.S. (1990). Automatic Number-Plate Recognition. Proceedings of the IEE Colloquium on Image Analysis for Transport Applications, IEEE."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"8868","DOI":"10.1109\/TITS.2021.3087158","article-title":"Rethinking and Designing a High-Performing Automatic License Plate Recognition Approach","volume":"23","author":"Wang","year":"2022","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Gon\u00e7alves, G.R., Menotti, D., and Schwartz, W.R. (2016). License Plate Recognition Based on Temporal Redundancy. Proceedings of the 2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC), IEEE.","DOI":"10.1109\/ITSC.2016.7795970"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/18\/5704\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,8]],"date-time":"2026-09-08T16:47:01Z","timestamp":1788886021000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/18\/5704"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9,8]]},"references-count":43,"journal-issue":{"issue":"18","published-online":{"date-parts":[[2026,9]]}},"alternative-id":["s26185704"],"URL":"https:\/\/doi.org\/10.3390\/s26185704","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,9,8]]}}}