{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,9]],"date-time":"2026-05-09T17:15:22Z","timestamp":1778346922231,"version":"3.51.4"},"reference-count":57,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2023,2,13]],"date-time":"2023-02-13T00:00:00Z","timestamp":1676246400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Prince Sultan University"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Video streaming-based real-time vehicle identification and license plate recognition systems are challenging to design and deploy in terms of real-time processing on edge, dealing with low image resolution, high noise, and identification. This paper addresses these issues by introducing a novel multi-stage, real-time, deep learning-based vehicle identification and license plate recognition system. The system is based on a set of algorithms that efficiently integrate two object detectors, an image classifier, and a multi-object tracker to recognize car models and license plates. The information redundancy of Saudi license plates\u2019 Arabic and English characters is leveraged to boost the license plate recognition accuracy while satisfying real-time inference performance. The system optimally achieves real-time performance on edge GPU devices and maximizes models\u2019 accuracy by taking advantage of the temporally redundant information of the video stream\u2019s frames. The edge device sends a notification of the detected vehicle and its license plate only once to the cloud after completing the processing. The system was experimentally evaluated on vehicles and license plates in real-world unconstrained environments at several parking entrance gates. It achieves 17.1 FPS on a Jetson Xavier AGX edge device with no delay. The comparison between the accuracy on the videos and on static images extracted from them shows that the processing of video streams using this proposed system enhances the relative accuracy of the car model and license plate recognition by 13% and 40%, respectively. This research work has won two awards in 2021 and 2022.<\/jats:p>","DOI":"10.3390\/s23042120","type":"journal-article","created":{"date-parts":[[2023,2,14]],"date-time":"2023-02-14T01:41:06Z","timestamp":1676338866000},"page":"2120","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":54,"title":["A Multi-Stage Deep-Learning-Based Vehicle and License Plate Recognition System with Real-Time Edge Inference"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0795-132X","authenticated-orcid":false,"given":"Adel","family":"Ammar","sequence":"first","affiliation":[{"name":"Robotics and Internet-of-Things Lab, Prince Sultan University, Riyadh 11586, Saudi Arabia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3787-7423","authenticated-orcid":false,"given":"Anis","family":"Koubaa","sequence":"additional","affiliation":[{"name":"Robotics and Internet-of-Things Lab, Prince Sultan University, Riyadh 11586, Saudi Arabia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2133-0757","authenticated-orcid":false,"given":"Wadii","family":"Boulila","sequence":"additional","affiliation":[{"name":"Robotics and Internet-of-Things Lab, Prince Sultan University, Riyadh 11586, Saudi Arabia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3057-4924","authenticated-orcid":false,"given":"Bilel","family":"Benjdira","sequence":"additional","affiliation":[{"name":"Robotics and Internet-of-Things Lab, Prince Sultan University, Riyadh 11586, Saudi Arabia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yasser","family":"Alhabashi","sequence":"additional","affiliation":[{"name":"Robotics and Internet-of-Things Lab, Prince Sultan University, Riyadh 11586, Saudi Arabia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,2,13]]},"reference":[{"key":"ref_1","unstructured":"(2022, June 06). AI Surveillance Camera Market 2022. Available online: https:\/\/southeast.newschannelnebraska.com\/story\/45571414\/ai-surveillance-camera%C2%A0."},{"key":"ref_2","unstructured":"(2022, June 06). ANPR System Market (2022\u20132027). Available online: https:\/\/www.marketsandmarkets.com\/Market-Reports\/anpr-system-market-140920103.html."},{"key":"ref_3","unstructured":"(2022, June 06). The Winners of the KAUST Challenge\u2014Ideas & Solutions For Hajj & Umrah 2020. Available online: https:\/\/challenge.kaust.edu.sa\/assets\/pdfs\/WINNER%20EN.pdf."},{"key":"ref_4","unstructured":"(2022, June 06). Oyoon Wins Best AI Product Award at the Saudi International Artificial Intelligence & Cloud Expo 2022. Available online: https:\/\/www.riotu-lab.org\/newsDetails.php?id=12."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"172443","DOI":"10.1109\/ACCESS.2019.2956172","article-title":"A survey of vehicle re-identification based on deep learning","volume":"7","author":"Wang","year":"2019","journal-title":"IEEE Access"},{"key":"ref_6","first-page":"1","article-title":"Vision-based Autonomous Vehicle Recognition: A New Challenge for Deep Learning-based Systems","volume":"54","author":"Boukerche","year":"2021","journal-title":"ACM Comput. Surv. (CSUR)"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Llorca, D.F., Col\u00e1s, D., Daza, I.G., Parra, I., and Sotelo, M.A. (2014, January 8\u201311). Vehicle model recognition using geometry and appearance of car emblems from rear view images. Proceedings of the 17th International IEEE Conference on Intelligent Transportation Systems (ITSC), Qingdao, China.","DOI":"10.1109\/ITSC.2014.6958187"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Lee, H.J., Ullah, I., Wan, W., Gao, Y., and Fang, Z. (2019). Real-time vehicle make and model recognition with the residual SqueezeNet architecture. Sensors, 19.","DOI":"10.3390\/s19050982"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"611","DOI":"10.3390\/make1020036","article-title":"Real-time vehicle make and model recognition system","volume":"1","author":"Manzoor","year":"2019","journal-title":"Mach. Learn. Knowl. Extr."},{"key":"ref_10","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":"2020","journal-title":"IEEE Access"},{"key":"ref_11","unstructured":"Liu, X., Liu, W., Mei, T., and Ma, H. (2016). European Conference on Computer Vision, Springer."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Selmi, Z., Halima, M.B., and Alimi, A.M. (2017, January 9\u201315). Deep learning system for automatic license plate detection and recognition. Proceedings of the 2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR), Kyoto, Japan.","DOI":"10.1109\/ICDAR.2017.187"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"159","DOI":"10.1016\/j.eswa.2019.06.036","article-title":"A two-stage deep neural network for multi-norm license plate detection and recognition","volume":"136","author":"Kessentini","year":"2019","journal-title":"Expert Syst. Appl."},{"key":"ref_14","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, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.690"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1016\/j.imavis.2019.04.007","article-title":"Automatic License Plate Recognition via sliding-window darknet-YOLO deep learning","volume":"87","author":"Hendry","year":"2019","journal-title":"Image Vis. Comput."},{"key":"ref_16","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, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.91"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Sarfraz, M., Ahmed, M., and Ghazi, S. (2003, January 16\u201318). Saudi Arabian license plate recognition system. Proceedings of the 2003 International Conference on Geometric Modeling and Graphics, London, UK.","DOI":"10.1109\/GMAG.2003.1219663"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Ahmed, M.J., Sarfraz, M., Zidouri, A., and Al-Khatib, W.G. (2003, January 14\u201317). License plate recognition system. Proceedings of the 10th IEEE International Conference on Electronics, Circuits and Systems, 2003 (ICECS 2003), Sharjah, United Arab Emirates.","DOI":"10.1109\/ICECS.2003.1301932"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Zidouri, A., and Deriche, M. (2008, January 23\u201326). Recognition of Arabic license plates using NN. Proceedings of the 2008 First Workshops on Image Processing Theory, Tools and Applications, Sousse, Tunisia.","DOI":"10.1109\/IPTA.2008.4743757"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Khan, I.R., Ali, S.T.A., Siddiq, A., Khan, M.M., Ilyas, M.U., Alshomrani, S., and Rahardja, S. (2022). Automatic License Plate Recognition in Real-World Traffic Videos Captured in Unconstrained Environment by a Mobile Camera. Electronics, 11.","DOI":"10.3390\/electronics11091408"},{"key":"ref_21","unstructured":"Driss, M., Almomani, I., Al-Suhaimi, R., and Al-Harbi, H. (2022). International Conference of Reliable Information and Communication Technology, Springer."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1073","DOI":"10.1016\/j.matpr.2022.09.165","article-title":"Automatic number plate detection using TensorFlow in Indian scenario: An optical character recognition approach","volume":"72","author":"Tote","year":"2023","journal-title":"Mater. Today Proc."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"200","DOI":"10.1049\/iet-ipr.2017.0368","article-title":"License number plate recognition system using entropy-based features selection approach with SVM","volume":"12","author":"Khan","year":"2018","journal-title":"IET Image Process."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"454","DOI":"10.7763\/IJCEE.2011.V3.360","article-title":"Automatic arabic license plate recognition","volume":"3","author":"Alginahi","year":"2011","journal-title":"Int. J. Comput. Electr. Eng."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1","DOI":"10.7763\/IJCEE.2013.V5.649","article-title":"Saudi license plate recognition","volume":"5","author":"Basalamah","year":"2013","journal-title":"Int. J. Comput. Electr. Eng."},{"key":"ref_26","first-page":"32","article-title":"The Kingdom of Saudi Arabia Vehicle License Plate Recognition using Learning Vector Quantization Artificial Neural Network","volume":"98","author":"Perwej","year":"2014","journal-title":"Int. J. Comput. Appl."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Alyahya, H.M., Alharthi, M.K., Alattas, A.M., and Thayananthan, V. (2017, January 6\u20137). Saudi license plate recognition system using artificial neural network classifier. Proceedings of the 2017 International Conference on Computer and Applications (ICCA), Doha, Qatar.","DOI":"10.1109\/COMAPP.2017.8079759"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Alzubaidi, L., Latif, G., and Alghazo, J. (2019, January 9\u201311). Affordable and portable realtime saudi license plate recognition using SoC. Proceedings of the 2019 2nd International Conference on New Trends in Computing Sciences (ICTCS), Amman, Jordan.","DOI":"10.1109\/ICTCS.2019.8923061"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"8266","DOI":"10.48084\/etasr.4727","article-title":"Automatic Number Plate Recognition of Saudi License Car Plates","volume":"12","author":"Antar","year":"2022","journal-title":"Eng. Technol. Appl. Sci. Res."},{"key":"ref_30","unstructured":"Bochkovskiy, A., Wang, C.Y., and Liao, H.Y.M. (2020). Yolov4: Optimal speed and accuracy of object detection. arXiv."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Ammar, A., Koubaa, A., and Benjdira, B. (2021). Deep-Learning-based Automated Palm Tree Counting and Geolocation in Large Farms from Aerial Geotagged Images. Agronomy, 11.","DOI":"10.3390\/agronomy11081458"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Ammar, A., Koubaa, A., Ahmed, M., Saad, A., and Benjdira, B. (2021). Vehicle detection from aerial images using deep learning: A comparative study. Electronics, 10.","DOI":"10.3390\/electronics10070820"},{"key":"ref_33","unstructured":"Redmon, J., and Farhadi, A. (2018). Yolov3: An incremental improvement. arXiv."},{"key":"ref_34","unstructured":"Ren, S., He, K., Girshick, R., and Sun, J. (2015, January 7\u201312). Faster R-CNN: Towards real-time object detection with region proposal networks. Proceedings of the 29th Annual Conference on Neural Information Processing Systems, Montreal, Canada."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Tan, M., Pang, R., and Le, Q.V. (2020, January 13\u201319). Efficientdet: Scalable and efficient object detection. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.01079"},{"key":"ref_36","unstructured":"Lin, T.Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Doll\u00e1r, P., and Zitnick, C.L. (2014). European Conference on Computer Vision, Springer."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Yadav, S., and Shukla, S. (2016, January 27\u201328). Analysis of k-Fold Cross-Validation over Hold-Out Validation on Colossal Datasets for Quality Classification. Proceedings of the 2016 IEEE 6th International Conference on Advanced Computing (IACC), Bhimavaram, India.","DOI":"10.1109\/IACC.2016.25"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Chollet, F. (2017, January 21\u201326). Xception: Deep learning with depthwise separable convolutions. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.195"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., and Fei-Fei, L. (2009, January 20\u201325). ImageNet: A large-scale hierarchical image database. Proceedings of the 2009 IEEE Conference on Computer Vision and Pattern Recognition, Miami, FL, USA.","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., and Chen, L.C. (2018, January 18\u201323). MobileNetV2: Inverted residuals and linear bottlenecks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00474"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Wojke, N., Bewley, A., and Paulus, D. (2017, January 17\u201320). Simple online and realtime tracking with a deep association metric. Proceedings of the 2017 IEEE International Conference on Image Processing (ICIP), Beijing, China.","DOI":"10.1109\/ICIP.2017.8296962"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Bewley, A., Ge, Z., Ott, L., Ramos, F., and Upcroft, B. (2016, January 25\u201328). Simple online and realtime tracking. Proceedings of the 2016 IEEE International Conference on Image Processing (ICIP), Phoenix, AZ, USA.","DOI":"10.1109\/ICIP.2016.7533003"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"143","DOI":"10.1109\/TNSE.2021.3055835","article-title":"Cloud Versus Edge Deployment Strategies of Real-Time Face Recognition Inference","volume":"9","author":"Koubaa","year":"2022","journal-title":"IEEE Trans. Netw. Sci. Eng."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"1341","DOI":"10.1162\/neco.1996.8.7.1341","article-title":"The lack of a priori distinctions between learning algorithms","volume":"8","author":"Wolpert","year":"1996","journal-title":"Neural Comput."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Hsu, H.K., Yao, C.H., Tsai, Y.H., Hung, W.C., Tseng, H.Y., Singh, M., and Yang, M.H. (2020, January 2\u20135). Progressive domain adaptation for object detection. Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, Snowmass Village, Colorado.","DOI":"10.1109\/WACV45572.2020.9093358"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Benjdira, B., Bazi, Y., Koubaa, A., and Ouni, K. (2019). Unsupervised domain adaptation using generative adversarial networks for semantic segmentation of aerial images. Remote Sens., 11.","DOI":"10.3390\/rs11111369"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Benjdira, B., Ammar, A., Koubaa, A., and Ouni, K. (2020). Data-efficient domain adaptation for semantic segmentation of aerial imagery using generative adversarial networks. Appl. Sci., 10.","DOI":"10.3390\/app10031092"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"257","DOI":"10.1007\/s11760-022-02228-w","article-title":"Video quality enhancement using recursive deep residual learning network","volume":"17","author":"Ayoub","year":"2022","journal-title":"Signal Image Video Process."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"017004","DOI":"10.1117\/1.2160515","article-title":"Objective video quality assessment","volume":"45","author":"Lee","year":"2006","journal-title":"Opt. Eng."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"165","DOI":"10.1109\/TBC.2011.2104671","article-title":"Objective video quality assessment methods: A classification, review, and performance comparison","volume":"57","author":"Chikkerur","year":"2011","journal-title":"IEEE Trans. Broadcast."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1007\/s11042-012-1199-5","article-title":"Quality assessment for a visual and automatic license plate recognition","volume":"68","author":"Janowski","year":"2014","journal-title":"Multimed. Tools Appl."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Leszczuk, M., Janowski, L., Nawa\u0142a, J., and Boev, A. (2022, January 3\u20134). Method for Assessing Objective Video Quality for Automatic License Plate Recognition Tasks. Proceedings of the Multimedia Communications, Services and Security: 11th International Conference, MCSS 2022, Krak\u00f3w, Poland.","DOI":"10.1007\/978-3-031-20215-5_13"},{"key":"ref_53","unstructured":"Ukhanova, A., St\u00f8ttrup-Andersen, J., Forchhammer, S., and Madsen, J. (2014, January 5\u20138). Quality assessment of compressed video for automatic license plate recognition. Proceedings of the 2014 International Conference on Computer Vision Theory and Applications (VISAPP), Lisbon, Portugal."},{"key":"ref_54","unstructured":"\u0141ubkowski, P., and Laskowski, D. (2017). Smart Solutions in Today\u2019s Transport: 17th International Conference on Transport Systems Telematics, TST 2017, Katowice, Poland, 5\u20138 April 2017, Springer. Selected Papers 17."},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Boulila, B., Khlifi, M., Ammar, A., Koubaa, A., Benjdira, B., and Farah, I.F. (2022). A Hybrid Privacy-Preserving Deep Learning Approach for Object Classification in Very High-Resolution Satellite Images. Remote Sens., 14.","DOI":"10.3390\/rs14184631"},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Boulila, B., Ammar, A., Benjdira, B., and Koubaa, A. (2022, January 9\u201311). Securing the classification of covid-19 in chest x-ray images: A privacy-preserving deep learning approach. Proceedings of the 2022 International Conference of Smart Systems and Emerging Technologies (SMARTTECH), Riyadh, Saudi Arabia.","DOI":"10.1109\/SMARTTECH54121.2022.00055"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"4322","DOI":"10.1109\/TNSE.2022.3199235","article-title":"A Novel Chaos-Based Privacy-Preserving Deep Learning Model for Cancer Diagnosis","volume":"9","author":"Rehman","year":"2022","journal-title":"IEEE Trans. Netw. Sci. 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