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Especially in countries that imposed a lockdown (mobility restrictions help reduce the spread of COVID\u201019), it becomes important to curtail the movement of vehicles as much as possible. For an effective visual traffic surveillance system, it is essential to detect vehicles from the images and classify the vehicles into different types (e.g., bus, car, and pickup truck). Most of the existing research studies focused only on maximizing the percentage of predictions, which have poor real\u2010time performance and consume more computing resources. To highlight the problems of classifying imbalanced data, a new technique is proposed in this research article for vehicle type classification. Initially, the data are collected from the Beijing Institute of Technology Vehicle Dataset and the MIOvision Traffic Camera Dataset. In addition, adaptive histogram equalization and the Gaussian mixture model are implemented for enhancing the quality of collected vehicle images and to detect vehicles from the denoised images. Then, the Steerable Pyramid Transform and the Weber Local Descriptor are employed to extract the feature vectors from the detected vehicles. Finally, the extracted features are given as the input to an ensemble deep learning technique for vehicle classification. In the simulation phase, the proposed ensemble deep learning technique obtained 99.13% and 99.28% of classification accuracy on the MIOvision Traffic Camera Dataset and the Beijing Institute of Technology Vehicle Dataset. The obtained results are effective compared to the standard existing benchmark techniques on both datasets.<\/jats:p>","DOI":"10.1155\/2021\/5590894","type":"journal-article","created":{"date-parts":[[2021,5,27]],"date-time":"2021-05-27T20:07:23Z","timestamp":1622146043000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":93,"title":["Moving Vehicle Detection and Classification Using Gaussian Mixture Model and Ensemble Deep Learning Technique"],"prefix":"10.1155","volume":"2021","author":[{"given":"Preetha","family":"Jagannathan","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8887-8912","authenticated-orcid":false,"given":"Sujatha","family":"Rajkumar","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6065-3087","authenticated-orcid":false,"given":"Jaroslav","family":"Frnda","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3997-5070","authenticated-orcid":false,"given":"Parameshachari Bidare","family":"Divakarachari","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5526-0884","authenticated-orcid":false,"given":"Prabu","family":"Subramani","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2021,5,27]]},"reference":[{"key":"e_1_2_9_1_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2016.09.116"},{"key":"e_1_2_9_2_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jvlc.2014.02.001"},{"key":"e_1_2_9_3_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijleo.2016.05.092"},{"key":"e_1_2_9_4_2","first-page":"1","article-title":"Fine-grained vehicle type classification using lightweight convolutional neural network with feature optimization and joint learning strategy","author":"Sun W.","year":"2020","journal-title":"Multimedia Tools and Applications"},{"key":"e_1_2_9_5_2","first-page":"1","article-title":"Multiple vehicle tracking and classification system with a convolutional neural network","author":"Kim H.","year":"2019","journal-title":"Journal of Ambient Intelligence and Humanized Computing"},{"key":"e_1_2_9_6_2","doi-asserted-by":"publisher","DOI":"10.1186\/s13640-018-0245-2"},{"key":"e_1_2_9_7_2","doi-asserted-by":"publisher","DOI":"10.1007\/s12652-020-01824-3"},{"key":"e_1_2_9_8_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-020-05331-6"},{"key":"e_1_2_9_9_2","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2012.2213814"},{"key":"e_1_2_9_10_2","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2019.2906821"},{"key":"e_1_2_9_11_2","doi-asserted-by":"publisher","DOI":"10.3390\/s20092559"},{"key":"e_1_2_9_12_2","first-page":"1","article-title":"Cyberbullying detection solutions based on deep learning architectures","author":"Iwendi C.","year":"2020","journal-title":"Multimedia Systems"},{"key":"e_1_2_9_13_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2018.06.035"},{"key":"e_1_2_9_14_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2015.12.134"},{"key":"e_1_2_9_15_2","doi-asserted-by":"publisher","DOI":"10.1007\/s12065-018-0167-z"},{"key":"e_1_2_9_16_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11554-017-0712-5"},{"key":"e_1_2_9_17_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00138-017-0846-2"},{"key":"e_1_2_9_18_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11277-018-5347-8"},{"key":"e_1_2_9_19_2","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2015.2402438"},{"key":"e_1_2_9_20_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.2997286"},{"key":"e_1_2_9_21_2","doi-asserted-by":"publisher","DOI":"10.1109\/TVT.2020.3000306"},{"key":"e_1_2_9_22_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2963486"},{"key":"e_1_2_9_23_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2950162"},{"key":"e_1_2_9_24_2","doi-asserted-by":"publisher","DOI":"10.1088\/1742-6596\/1069\/1\/012099"},{"key":"e_1_2_9_25_2","doi-asserted-by":"publisher","DOI":"10.26552\/com.C.2020.3.119-127"},{"key":"e_1_2_9_26_2","doi-asserted-by":"crossref","unstructured":"RoeckerM. 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