{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,8]],"date-time":"2025-11-08T17:59:00Z","timestamp":1762624740304,"version":"build-2065373602"},"reference-count":43,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2021,11,30]],"date-time":"2021-11-30T00:00:00Z","timestamp":1638230400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Future Internet"],"abstract":"<jats:p>Object counting is an active research area that gained more attention in the past few years. In smart cities, vehicle counting plays a crucial role in urban planning and management of the Intelligent Transportation Systems (ITS). Several approaches have been proposed in the literature to address this problem. However, the resulting detection accuracy is still not adequate. This paper proposes an efficient approach that uses deep learning concepts and correlation filters for multi-object counting and tracking. The performance of the proposed system is evaluated using a dataset consisting of 16 videos with different features to examine the impact of object density, image quality, angle of view, and speed of motion towards system accuracy. Performance evaluation exhibits promising results in normal traffic scenarios and adverse weather conditions. Moreover, the proposed approach outperforms the performance of two recent approaches from the literature.<\/jats:p>","DOI":"10.3390\/fi13120306","type":"journal-article","created":{"date-parts":[[2021,11,30]],"date-time":"2021-11-30T20:29:27Z","timestamp":1638304167000},"page":"306","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["An Advanced Deep Learning Approach for Multi-Object Counting in Urban Vehicular Environments"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9834-2732","authenticated-orcid":false,"given":"Ahmed","family":"Dirir","sequence":"first","affiliation":[{"name":"College of Information Technology, UAE University, Al Ain, Abu Dhabi 15551, United Arab Emirates"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Henry","family":"Ignatious","sequence":"additional","affiliation":[{"name":"College of Information Technology, UAE University, Al Ain, Abu Dhabi 15551, United Arab Emirates"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7488-0915","authenticated-orcid":false,"given":"Hesham","family":"Elsayed","sequence":"additional","affiliation":[{"name":"College of Information Technology, UAE University, Al Ain, Abu Dhabi 15551, United Arab Emirates"},{"name":"Emirates Center for Mobility Research, UAE University, Al Ain, Abu Dhabi 15551, United Arab Emirates"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0319-8126","authenticated-orcid":false,"given":"Manzoor","family":"Khan","sequence":"additional","affiliation":[{"name":"College of Information Technology, UAE University, Al Ain, Abu Dhabi 15551, United Arab Emirates"},{"name":"Emirates Center for Mobility Research, UAE University, Al Ain, Abu Dhabi 15551, United Arab Emirates"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mohammed","family":"Adib","sequence":"additional","affiliation":[{"name":"College of Information Technology, UAE University, Al Ain, Abu Dhabi 15551, United Arab Emirates"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Anas","family":"Mahmoud","sequence":"additional","affiliation":[{"name":"College of Information Technology, UAE University, Al Ain, Abu Dhabi 15551, United Arab Emirates"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Moatasem","family":"Al-Gunaid","sequence":"additional","affiliation":[{"name":"College of Information Technology, UAE University, Al Ain, Abu Dhabi 15551, United Arab Emirates"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,11,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1145\/3360911","article-title":"Real-world applications for drones","volume":"62","author":"Kugler","year":"2019","journal-title":"Commun. ACM"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Rosser, J.C., Vignesh, V., Terwilliger, B.A., and Parker, B.C. (2018). Surgical and medical applications of drones: A comprehensive review. J. Soc. Laparoendosc. Surg., 22, Available online: https:\/\/pubmed.ncbi.nlm.nih.gov\/30356360\/.","DOI":"10.4293\/JSLS.2018.00018"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"99","DOI":"10.1016\/j.paerosci.2017.04.003","article-title":"Classifications, applications, and design challenges of drones: A review","volume":"91","author":"Hassanalian","year":"2017","journal-title":"Prog. Aerosp. Sci."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1016\/j.paerosci.2018.01.003","article-title":"Evolution of space drones for planetary exploration: A review","volume":"97","author":"Hassanalian","year":"2018","journal-title":"Prog. Aerosp. Sci."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Mirthubashini, J., and Santhi, V. (2020, January 6\u20137). Video Based Vehicle Counting Using Deep Learning Algorithms. Proceedings of the 2020 6th International Conference on Advanced Computing and Communication Systems (ICACCS), Coimbatore, India.","DOI":"10.1109\/ICACCS48705.2020.9074280"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"64460","DOI":"10.1109\/ACCESS.2019.2914254","article-title":"Video-Based Vehicle Counting Framework","volume":"7","author":"Dai","year":"2019","journal-title":"IEEE Access"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Yang, Y., and Gao, W. (2019, January 18\u201320). A Method of Pedestrians Counting Based on Deep Learning. Proceedings of the 2019 3rd International Conference on Electronic Information Technology and Computer Engineering (EITCE), Xiamen, China.","DOI":"10.1109\/EITCE47263.2019.9094838"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"84252","DOI":"10.1109\/ACCESS.2021.3088075","article-title":"YOLO-Based Deep Learning Framework for Olive Fruit Fly Detection and Counting","volume":"9","author":"Mamdouh","year":"2021","journal-title":"IEEE Access"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Dirir, A., Adib, M., Mahmoud, A., Al-Gunaid, M., and El-Sayed, H. (2021, January 16\u201318). An Efficient Multi-Object Tracking and Counting Framework Using Video Streaming in Urban Vehicular Environments. Proceedings of the 2020 International Conference on Communications, Signal Processing, and their Applications (ICCSPA), Sharjah, United Arab Emirates.","DOI":"10.1109\/ICCSPA49915.2021.9385732"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Farkhodov, K., Lee, S.-H., and Kwon, K.-R. (2021, November 27). Object Tracking Using CSRT Tracker and RCNN Farkhodov2020object. Available online: https:\/\/www.scitepress.org\/Papers\/2020\/91838\/91838.pdf.","DOI":"10.5220\/0009183802090212"},{"key":"ref_11","first-page":"3099","article-title":"Background subtraction techniques: A review","volume":"4","author":"Piccardi","year":"2004","journal-title":"IEEE Proc. Int. Conf. Syst. Man Cybern."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Del Blanco, C.R., Jaureguizar, F., and Garc\u00eda, N. (2010, January 26\u201329). Visual tracking of multiple interacting objects through raoblackwellized data association particle filtering. Proceedings of the 2010 IEEE International Conference on Image Processing, Hong Kong, China. Available online: https:\/\/ieeexplore.ieee.org\/document\/5653411.","DOI":"10.1109\/ICIP.2010.5653411"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Genovesio, A., and Olivo-Marin, J.C. (2004, January 26). Split and merge data association filter for dense multi-target tracking. Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004, Cambridge, UK.","DOI":"10.1109\/ICPR.2004.1333863"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"580","DOI":"10.1016\/j.cviu.2009.01.002","article-title":"Target tracking with incomplete detection","volume":"113","author":"Ma","year":"2009","journal-title":"Comp. Vis. Image Underst."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"2196","DOI":"10.1109\/TPAMI.2008.253","article-title":"Multiple-target tracking by spatiotemporal monte carlo markov chain data association","volume":"31","author":"Yu","year":"2009","journal-title":"IEEE Trans. Patern Anal. Mach. Intell."},{"key":"ref_16","unstructured":"Khan, Z., Balch, T., and Dellaert, F. (2005, January 20\u201325). Multitarget tracking with split and merged measurements. Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR\u201905), San Diego, CA, USA."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Del Blanco, C.R., Jaureguizar, F., and Garc\u00eda, N. (2011, January 11\u201314). Bayesian Visual Surveillance: A Model for Detecting and Tracking a variable number of moving objects. Proceedings of the 2011 18th IEEE International Conference on Image Processing, Brussels, Belgium. Available online: https:\/\/ieeexplore.ieee.org\/document\/6115712.","DOI":"10.1109\/ICIP.2011.6115712"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Yam, K., Siu, W., Law, N., and Chan, C. (2011, January 9\u201312). Effective bidirectional people flow counting for real time surveillance system. Proceedings of the 2011 IEEE International Conference on Consumer Electronics (ICCE), Las Vegas, NV, USA.","DOI":"10.1109\/ICCE.2011.5722907"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Tuzel, O., Porikli, F., and Meer, P. (2008, January 23\u201328). Learning on lie groups for invariant detection and tracking. Proceedings of the 2008 IEEE Conference on Computer Vision and Pattern Recognition, Anchorage, AK, USA. Available online: https:\/\/ieeexplore.ieee.org\/document\/4587521.","DOI":"10.1109\/CVPR.2008.4587521"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Galvez, R.L., Bandala, A.A., Dadios, E.P., Vicerra, R.R., and Maningo, J.M. (2018, January 28). Object Detection Using Convolutional Neural Networks. Proceedings of the TENCON 2018\u20142018 IEEE Region 10 Conference, Jeju, Korea. Available online: https:\/\/ieeexplore.ieee.org\/abstract\/document\/8650517.","DOI":"10.1109\/TENCON.2018.8650517"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"241","DOI":"10.1016\/j.ins.2020.02.067","article-title":"DC-SPP-YOLO: Dense connection and spatial pyramid pooling based YOLO for object detection","volume":"522","author":"Huang","year":"2020","journal-title":"Inf. Sci."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1016\/j.neucom.2018.03.030","article-title":"Face detection using deep learning: An improved faster RCNN approach","volume":"299","author":"Sun","year":"2018","journal-title":"Neurocomputing"},{"key":"ref_23","unstructured":"Broad, A., Jones, M., and Lee, T.Y. (2021, November 27). Recurrent Multi-frame Single Shot Detector for Video Object Detection. In BMVC 2018 Sep. Available online: http:\/\/bmvc2018.org\/contents\/papers\/0309.pdf."},{"key":"ref_24","first-page":"46","article-title":"A Review: Object Detection using Deep Learning","volume":"180","author":"Naik","year":"2018","journal-title":"Int. J. Comput. Appl."},{"key":"ref_25","first-page":"18","article-title":"Object Detection using Deep Learning","volume":"182","author":"Anusha","year":"2018","journal-title":"Int. J. Comput. Appl."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1706","DOI":"10.1016\/j.procs.2018.05.144","article-title":"Application of Deep Learning for Object Detection","volume":"132","author":"Pathak","year":"2018","journal-title":"Procedia Comput. Sci."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Redmon, J., Divvala, S., Girshick, R., and Farhadi, A. (2016). You Only Look Once: Unified, Real-Time Object Detection. Proc. Cvpr, 779\u2013788. Available online: https:\/\/www.cv-foundation.org\/openaccess\/content_cvpr_2016\/html\/Redmon_You_Only_Look_CVPR_2016_paper.html.","DOI":"10.1109\/CVPR.2016.91"},{"key":"ref_28","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_29","unstructured":"Redmon, J., and Farhadi, A. (2018). Yolov3: An incremental improvement. arXiv."},{"key":"ref_30","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. Available online: https:\/\/link.springer.com\/chapter\/10.1007\/978-3-319-46448-0_2.","DOI":"10.1007\/978-3-319-46448-0_2"},{"key":"ref_31","unstructured":"Fu, C.-Y., Liu, W., Ranga, A., and Tyagi, A. (2017). Berg.Dssd: Deconvolutional single shots detector. arXiv."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"He, K., Gkioxari, G., Dollar, P., and Girshick, R. (2017). Mask R-CNN. Proceedings of ICCV, Available online: https:\/\/openaccess.thecvf.com\/content_iccv_2017\/html\/He_Mask_R-CNN_ICCV_2017_paper.html.","DOI":"10.1109\/ICCV.2017.322"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Sunkara, J., Santhosh, M., Cherukuri, S., and Gopi Krishna, L. (2017, January 21\u201322). Object Tracking Techniques and Performance Measures\u2014A Conceptual Survey. Proceedings of the IEEE International Conference on Power, Control, Signals and Instrumentation Engineering (ICPCSI-2017), Chennai, India.","DOI":"10.1109\/ICPCSI.2017.8392127"},{"key":"ref_34","unstructured":"Zhang, D., Maei, H., Wang, X., and Wang, Y. (2017). Deep reinforcement learning for visual object tracking in videos. Comput. Res. Repos., Available online: https:\/\/arxiv.org\/pdf\/1701.08936.pdf."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Saxena, G., Gupta, A., Verma, D.K., Rajan, A., and Rawat, A. (2019, January 13\u201314). Robust Algorithms for Counting and Detection of Moving Vehicles using Deep Learning. Proceedings of the 2019 IEEE 9th International Conference on Advanced Computing (IACC), Tiruchirappalli, India.","DOI":"10.1109\/IACC48062.2019.8971573"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Hardjono, B., and Tjahyadi, H. Vehicle Counting Quantitative Comparison Using Background Subtraction, Viola Jones and Deep Learning Methods. Proceedings of the 2018 IEEE 9th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON), Available online: https:\/\/ieeexplore.ieee.org\/abstract\/document\/8615085.","DOI":"10.1109\/IEMCON.2018.8615085"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Lin, J.-P., and Sun, M.-T. (December, January 30). A YOLO-Based Traffic Counting System. Proceedings of the 2018 Conference on Technologies and Applications of Artificial Intelligence (TAAI), Taichung, Taiwan.","DOI":"10.1109\/TAAI.2018.00027"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Asha, C.S., and Narasimhadhan, A.V. Vehicle Counting for Traffic Management System using YOLO and Correlation Filter. Proceedings of the 2018 IEEE International Conference on Electronics, Computing and Communication Technologies (CONECCT), Available online: https:\/\/ieeexplore.ieee.org\/abstract\/document\/8482380.","DOI":"10.1109\/CONECCT.2018.8482380"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Forero, A., and Calderon, F. (2019, January 24\u201326). Vehicle and pedestrian video-tracking with classification based on deep convolutional neural networks. Proceedings of the 2019 XXII Symposium on Image, Signal Processing and Artificial Vision (STSIVA), Bucaramanga, Colombia.","DOI":"10.1109\/STSIVA.2019.8730234"},{"key":"ref_40","unstructured":"Mohamed, A.A. (2020, January 8\u20139). Accurate Vehicle Counting Approach Based on Deep Neural Networks. Proceedings of the 2019 International Conference on Innovative Trends in Computer Engineering (ITCE), Aswan, Egypt."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Amitha, I.C., and Narayanan, N.K. (2021). Object Detection Using YOLO Framework for Intelligent Traffic Monitoring. Machine Vision and Augmented Intelligence\u2014Theory and Applications, Springer.","DOI":"10.1007\/978-981-16-5078-9_34"},{"key":"ref_42","unstructured":"Can, V.X., Vu, P.X., Rui-fang, M., Thuat, V.T., Van Duy, V., and Noi, N.D. (2021, November 27). Vehicle Detection and Counting Under Mixed Traffic Conditions in Vietnam Using Yolov4. Available online: https:\/\/d1wqtxts1xzle7.cloudfront.net\/67640687\/IJARET_12_02_072.pdf?1623818829=&response-content-disposition=inline%3B+filename%3DVEHICLE_DETECTION_AND_COUNTING_UNDER_MIX.pdf&Expires=1638195611&Signature=A7yVUQcYKePsOgUZFH4zqVXbmsP0QpVRlDLAYnmHiCIEDdV6uo4VJS-1T945AeWp~IkEcwak8YlVah0TFMs9mw4rNFO3ISDAFnqciqzKZL2uFZqckHtJIdTSwwJrFDpSk1zgPep6yr8wKQw~6-abIhv-2-yWSOi0DAOzYtFuUzlShv~Z4mWUefyI-OZcxqfDj3SUkaTvELtGCZlrNtXHQa2s0RicIT3xw0mGGFf-6~u-xuaviKnFuCz9~dtn2XCmQEuAkOjCSDn3uQjJksZsA7U4HiUf~ziZ1G9ke~4u~Uv5n6YpAO4KpL0y3oOumdc71J~aHvBE0rzaYtk0rQOO1w__&Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1007\/s00607-020-00869-8","article-title":"An improved YOLO-based road traffic monitoring system","volume":"103","author":"Abbasi","year":"2021","journal-title":"Computing"}],"container-title":["Future Internet"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1999-5903\/13\/12\/306\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T07:37:45Z","timestamp":1760168265000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1999-5903\/13\/12\/306"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,11,30]]},"references-count":43,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2021,12]]}},"alternative-id":["fi13120306"],"URL":"https:\/\/doi.org\/10.3390\/fi13120306","relation":{},"ISSN":["1999-5903"],"issn-type":[{"type":"electronic","value":"1999-5903"}],"subject":[],"published":{"date-parts":[[2021,11,30]]}}}