2021
DOI: 10.1155/2021/5590894
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Moving Vehicle Detection and Classification Using Gaussian Mixture Model and Ensemble Deep Learning Technique

Abstract: In recent decades, automatic vehicle classification plays a vital role in intelligent transportation systems and visual traffic surveillance systems. Especially in countries that imposed a lockdown (mobility restrictions help reduce the spread of COVID-19), 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 … Show more

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Cited by 71 publications
(10 citation statements)
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“…Neural networks are often preferred for solving nonlinear problems [ 18 ]. FLANN is a high-order functional link–based single layer artificial neural network.…”
Section: Methodsmentioning
confidence: 99%
See 1 more Smart Citation
“…Neural networks are often preferred for solving nonlinear problems [ 18 ]. FLANN is a high-order functional link–based single layer artificial neural network.…”
Section: Methodsmentioning
confidence: 99%
“…Adel S. Assiri et al used a voting-based ensemble algorithm with the three best classifiers which are chosen based on their F3 score of 99.42% on the WBCD dataset [ 17 ]. T. Admassu optimized the KNN technique to optimize the Wisconsin Breast Cancer Dataset [ 18 ]. M. Zohaib et al demonstrated that with feature selection based on chi-square, the optimal value for KNN for WBC and WBCD datasets is in the range 1-9 with the Manhattan or Canberra distance functions to measure the distance between points [ 19 ].…”
Section: Related Workmentioning
confidence: 99%
“…Subsequently, they fed the extracted features to the ensemble of three RESnet variants to provide reliable classification results. Their model outperformed the state-of-the-art image-based classification models on the same testing set, especially for some of the minority classes ( 24 ). The aforementioned method demonstrated the effectiveness of using ensemble deep learning methods to handle the imbalanced data set issue in the vehicle classification problems.…”
Section: Literature Reviewmentioning
confidence: 96%
“…Their model achieved the same level of accuracy with the ensemble of three RESnet variants ( 19 ) and confirmed the effectiveness of the ensemble approach. Jagannathan et al highlighted the importance of solving the classification problem on an imbalanced data set ( 24 ). To diminish the overfitting issue in the DNN training, they first extracted features from vehicle images using a hybrid feature descriptor: the Steerable Pyramid Transform and the Weber Local Descriptor.…”
Section: Literature Reviewmentioning
confidence: 99%
“…However, some vision-based applications, such as real-time embedded systems, need a significant quantity of memory and fast processing rates. Indeed, segmentationbased road recognition is one of the most difficult problems in computer vision [8], which entails investigating and detecting the vehicle's surroundings. Unlike traditional approaches that rely on hand-crafted features such as edges and corners, deep learning models are trained incrementally using enormous amounts of data, automating the process of obtaining and training hierarchical feature representations.…”
Section: Introductionmentioning
confidence: 99%