{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,15]],"date-time":"2026-05-15T15:12:37Z","timestamp":1778857957542,"version":"3.51.4"},"reference-count":26,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2022,2,4]],"date-time":"2022-02-04T00:00:00Z","timestamp":1643932800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the National Agency of Road Safety (NARSA) and Moroccan Ministry of Equipment, Transport, Logistics and Water, via the National Center for Scientific and Technical Research (CNRST)","award":["Howdrive Project"],"award-info":[{"award-number":["Howdrive Project"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>There has been significant interest in using Convolutional Neural Networks (CNN) based methods for Automated Vehicular Surveillance (AVS) systems. Although these methods provide high accuracy, they are computationally expensive. On the other hand, Background Subtraction (BS)-based approaches are lightweight but provide insufficient information for tasks such as monitoring driving behavior and detecting traffic rules violations. In this paper, we propose a framework to reduce the complexity of CNN-based AVS methods, where a BS-based module is introduced as a preprocessing step to optimize the number of convolution operations executed by the CNN module. The BS-based module generates image-candidates containing only moving objects. A CNN-based detector with the appropriate number of convolutions is then applied to each image-candidate to handle the overlapping problem and improve detection performance. Four state-of-the-art CNN-based detection architectures were benchmarked as base models of the detection cores to evaluate the proposed framework. The experiments were conducted using a large-scale dataset. The computational complexity reduction of the proposed framework increases with the complexity of the considered CNN model\u2019s architecture (e.g., 30.6% for YOLOv5s with 7.3M parameters; 52.2% for YOLOv5x with 87.7M parameters), without undermining accuracy.<\/jats:p>","DOI":"10.3390\/s22031193","type":"journal-article","created":{"date-parts":[[2022,2,6]],"date-time":"2022-02-06T20:40:18Z","timestamp":1644180018000},"page":"1193","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":26,"title":["A Resource-Efficient CNN-Based Method for Moving Vehicle Detection"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2867-5491","authenticated-orcid":false,"given":"Zakaria","family":"Charouh","sequence":"first","affiliation":[{"name":"ERSC Team, Mohammadia Engineering School, Mohammed V University, Rabat 10 090, Morocco"},{"name":"TICLab, College of Engineering and Architecture, International University of Rabat, Rabat 11 100, Morocco"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4790-1442","authenticated-orcid":false,"given":"Amal","family":"Ezzouhri","sequence":"additional","affiliation":[{"name":"ERSC Team, Mohammadia Engineering School, Mohammed V University, Rabat 10 090, Morocco"},{"name":"TICLab, College of Engineering and Architecture, International University of Rabat, Rabat 11 100, Morocco"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mounir","family":"Ghogho","sequence":"additional","affiliation":[{"name":"TICLab, College of Engineering and Architecture, International University of Rabat, Rabat 11 100, Morocco"},{"name":"School of Electronic and Electrical Engineering, Faculty of Engineering, University of Leeds, Leeds LS2 9JT, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7142-0550","authenticated-orcid":false,"given":"Zouhair","family":"Guennoun","sequence":"additional","affiliation":[{"name":"ERSC Team, Mohammadia Engineering School, Mohammed V University, Rabat 10 090, Morocco"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,2,4]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"100204","DOI":"10.1016\/j.cosrev.2019.100204","article-title":"Background subtraction in real applications: Challenges, current models and future directions","volume":"35","author":"Bouwmans","year":"2020","journal-title":"Comput. Sci. Rev."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Charouh, Z., Ghogho, M., and Guennoun, Z. (2019, January 3\u20135). Improved background subtraction-based moving vehicle detection by optimizing morphological operations using machine learning. Proceedings of the 2019 IEEE International Symposium on Innovations in Intelligent Systems and Applications (INISTA), Sofia, Bulgaria.","DOI":"10.1109\/INISTA.2019.8778263"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Kim, J., and Ha, J.E. (2021). Foreground Objects Detection by U-Net with Multiple Difference Images. Appl. Sci., 11.","DOI":"10.3390\/app11041807"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Pardas, M., and Canet, G. (2021, January 18\u201321). Refinement Network for unsupervised on the scene Foreground Segmentation. Proceedings of the 2020 28th European Signal Processing Conference (EUSIPCO), Amsterdam, The Netherlands.","DOI":"10.23919\/Eusipco47968.2020.9287375"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Srigrarom, S., and Chew, K.H. (2022, January 1\u20134). Hybrid motion-based object detection for detecting and tracking of small and fast moving drones. Proceedings of the 2020 International Conference on Unmanned Aircraft Systems (ICUAS), Athens, Greece.","DOI":"10.1109\/ICUAS48674.2020.9213912"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Rabidas, R., Ravi, D.K., Pradhan, S., Moudgollya, R., and Ganguly, A. (2020, January 4\u20136). Investigation and Improvement of VGG based Encoder-Decoder Architecture for Background Subtraction. Proceedings of the 2020 Advanced Communication Technologies and Signal Processing (ACTS), Silchar, India.","DOI":"10.1109\/ACTS49415.2020.9350442"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Bakkay, M.C., Rashwan, H.A., Salmane, H., Khoudour, L., Puig, D., and Ruichek, Y. (2018, January 7\u201310). BSCGAN: Deep background subtraction with conditional generative adversarial networks. Proceedings of the 2018 25th IEEE International Conference on Image Processing (ICIP), Athens, Greece.","DOI":"10.1109\/ICIP.2018.8451603"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"635","DOI":"10.1016\/j.patcog.2017.09.040","article-title":"A deep convolutional neural network for video sequence BS","volume":"76","author":"Babaee","year":"2018","journal-title":"Pattern Recognit."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Wang, X., Liu, L., Li, G., Dong, X., Zhao, P., and Feng, X. (2018, January 8\u201313). Background subtraction on depth videos with convolutional neural networks. Proceedings of the 2018 International Joint Conference on Neural Networks (IJCNN), Rio de Janeiro, Brazil.","DOI":"10.1109\/IJCNN.2018.8489230"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Behnaz, R., Amirreza, F., and Ostadabbas, S. (2021, January 10\u201315). DeepPBM: Deep Probabilistic Background Model Estimation from Video Sequences. Proceedings of the International Conference on Pattern Recognition, Milan, Italy.","DOI":"10.1007\/978-3-030-68790-8_47"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"119144","DOI":"10.1109\/ACCESS.2020.3004495","article-title":"Background subtraction based on GAN and domain adaptation for VHR optical remote sensing videos","volume":"8","author":"Yu","year":"2020","journal-title":"IEEE Access"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Sultana, M., Mahmood, A., Bouwmans, T., and Jung, S.K. (2020). Unsupervised adversarial learning for dynamic background modeling. International Workshop on Frontiers of Computer Vision, Springer.","DOI":"10.1007\/978-981-15-4818-5_19"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Braham, M., and Van Droogenbroeck, M. (2016, January 23\u201325). Deep background subtraction with scene-specific convolutional neural networks. Proceedings of the 2016 International Conference on Systems, Signals and Image Processing (IWSSIP), Bratislava, Slovakia.","DOI":"10.1109\/IWSSIP.2016.7502717"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Xu, P., Ye, M., Li, X., Liu, Q., Yang, Y., and Ding, J. (2014, January 3\u20137). Dynamic background learning through deep auto-encoder networks. Proceedings of the 22nd ACM International Conference on Multimedia, Orlando, FL, USA.","DOI":"10.1145\/2647868.2654914"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"71","DOI":"10.18201\/ijisae.2020261587","article-title":"Object Recognition with Hybrid deep learning Methods and Testing on Embedded Systems","volume":"8","author":"Taspinar","year":"2020","journal-title":"Int. J. Intell. Syst. Appl. Eng."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Yu, T., Yang, J., and Lu, W. (2019). Combining background subtraction and Convolutional Neural Network for Anomaly Detection in Pumping-Unit Surveillance. Algorithms, 12.","DOI":"10.3390\/a12060115"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"802","DOI":"10.1016\/j.jvcir.2018.08.013","article-title":"Object detection from dynamic scene using joint background modeling and fast deep learning classification","volume":"55","author":"Yousif","year":"2018","journal-title":"J. Vis. Commun. Image Represent."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s40537-018-0131-x","article-title":"A hybrid framework combining background subtraction and deep neural networks for rapid person detection","volume":"5","author":"Kim","year":"2018","journal-title":"J. Big Data"},{"key":"ref_19","unstructured":"Redmon, J., and Farhadi, A. (2018). Yolov3: An incremental improvement. arXiv."},{"key":"ref_20","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_21","unstructured":"Dai, J., Li, Y., He, K., and Sun, J. (2016, January 5\u201310). R-fcn: Object detection via region-based fully convolutional networks. Proceedings of the Advances in Neural Information Processing Systems, Barcelona, Spain."},{"key":"ref_22","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_23","unstructured":"Bochkovskiy, A., Wang, C.Y., and Liao, H.Y.M. (2020). Yolov4: Optimal speed and accuracy of object detection. arXiv."},{"key":"ref_24","unstructured":"Long, X., Deng, K., Wang, G., Zhang, Y., Dang, Q., Gao, Y., and Wen, S. (2020). PP-YOLO: An effective and efficient implementation of object detector. arXiv."},{"key":"ref_25","unstructured":"Jocher, G., Nishimura, K., Mineeva, T., and Vilari\u00f1o, R. (2022, January 18). Yolov5. Code Repository. Available online: https:\/\/github.com\/ultralytics\/yolov5."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s12544-019-0390-4","article-title":"Vision-based vehicle detection and counting system using deep learning in highway scenes","volume":"11","author":"Song","year":"2019","journal-title":"Eur. Transp. Res. Rev."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/3\/1193\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T22:14:16Z","timestamp":1760134456000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/3\/1193"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,2,4]]},"references-count":26,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2022,2]]}},"alternative-id":["s22031193"],"URL":"https:\/\/doi.org\/10.3390\/s22031193","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,2,4]]}}}