Vehicle Classification Using Visual Background Extractor and Multi-class Support Vector Machines

Ng, Lee Teng; Suandi, Shahrel Azmin; Teoh, Soo Siang

doi:10.1007/978-981-4585-42-2_26

Cited by 17 publications

(5 citation statements)

References 8 publications

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“…In the early era of computer vision, handcrafted featuresbased vehicle classification methods have been proposed for intelligent transportation systems. In this regard, Ng et al [15] have proposed HOG-SVM based handcrafted features method to train SVM classifier using HOG features with Gaussian kernel function. e proposed classifier has been evaluated on 2800-image dataset of surveillance videos, which classified the motorcycle, car, and lorries with 92.3% accuracy.…”

Section: Related Workmentioning

confidence: 99%

Convolutional Neural Network Based Vehicle Classification in Adverse Illuminous Conditions for Intelligent Transportation Systems

et al. 2021

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In step with rapid advancements in computer vision, vehicle classification demonstrates a considerable potential to reshape intelligent transportation systems. In the last couple of decades, image processing and pattern recognition-based vehicle classification systems have been used to improve the effectiveness of automated highway toll collection and traffic monitoring systems. However, these methods are trained on limited handcrafted features extracted from small datasets, which do not cater the real-time road traffic conditions. Deep learning-based classification systems have been proposed to incorporate the above-mentioned issues in traditional methods. However, convolutional neural networks require piles of data including noise, weather, and illumination factors to ensure robustness in real-time applications. Moreover, there is no generalized dataset available to validate the efficacy of vehicle classification systems. To overcome these issues, we propose a convolutional neural network-based vehicle classification system to improve robustness of vehicle classification in real-time applications. We present a vehicle dataset comprising of 10,000 images categorized into six-common vehicle classes considering adverse illuminous conditions to achieve robustness in real-time vehicle classification systems. Initially, pretrained AlexNet, GoogleNet, Inception-v3, VGG, and ResNet are fine-tuned on self-constructed vehicle dataset to evaluate their performance in terms of accuracy and convergence. Based on better performance, ResNet architecture is further improved by adding a new classification block in the network. To ensure generalization, we fine-tuned the network on the public VeRi dataset containing 50,000 images, which have been categorized into six vehicle classes. Finally, a comparison study has been carried out between the proposed and existing vehicle classification methods to evaluate the effectiveness of the proposed vehicle classification system. Consequently, our proposed system achieved 99.68%, 99.65%, and 99.56% accuracy, precision, and F1-score on our self-constructed dataset.

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Section: Related Workmentioning

confidence: 99%

Convolutional Neural Network Based Vehicle Classification in Adverse Illuminous Conditions for Intelligent Transportation Systems

et al. 2021

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“…During the primary period of CV, customized feature-based approaches were expected for ITSs. Researchers used the HOG-SVM based customized features approach for training a SVM classifier utilizing HOG features along with the Gaussian Kernel feature suggested by Ng et al [21]. The aforementioned classification model was tested on a surveillance footage collection of 2800 images.…”

Section: Related Workmentioning

confidence: 99%

Bangladeshi Native Vehicle Classification Based on Transfer Learning with Deep Convolutional Neural Network

Hasan

Wang

Hussain

et al. 2021

Sensors

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Vehicle type classification plays an essential role in developing an intelligent transportation system (ITS). Based on the modern accomplishments of deep learning (DL) on image classification, we proposed a model based on transfer learning, incorporating data augmentation, for the recognition and classification of Bangladeshi native vehicle types. An extensive dataset of Bangladeshi native vehicles, encompassing 10,440 images, was developed. Here, the images are categorized into 13 common vehicle classes in Bangladesh. The method utilized was a residual network (ResNet-50)-based model, with extra classification blocks added to improve performance. Here, vehicle type features were automatically extracted and categorized. While conducting the analysis, a variety of metrics was used for the evaluation, including accuracy, precision, recall, and F1 − Score. In spite of the changing physical properties of the vehicles, the proposed model achieved progressive accuracy. Our proposed method surpasses the existing baseline method as well as two pre-trained DL approaches, AlexNet and VGG-16. Based on result comparisons, we have seen that, in the classification of Bangladeshi native vehicle types, our suggested ResNet-50 pre-trained model achieves an accuracy of 98.00%.

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“…In recent years, developing vision-based vehicle classification methods using machine learning (ML) has attracted many researchers, as it offers an efficient and adaptable approach that can fulfil the requirements of growing ITS applications. In this context, a large number of works that apply ML methods for vision-based vehicle classification have been proposed so far [10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25]. In [10], a method for vehicle image classification using neural network (NN) with conditional adaptive distance is presented.…”

Section: Introductionmentioning

confidence: 99%

“…In [10], a method for vehicle image classification using neural network (NN) with conditional adaptive distance is presented. The vehicle classification method based on the use of multi-class support vector machine (SVM) is proposed in [11]. In [12], another a vehicle classification method that adopts fuzzy support vector machine is provided.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning-Based Vehicle Classification for Low Quality Images

Tas

Sari

Dalveren

et al. 2022

Sensors

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This study proposes a simple convolutional neural network (CNN)-based model for vehicle classification in low resolution surveillance images collected by a standard security camera installed distant from a traffic scene. In order to evaluate its effectiveness, the proposed model is tested on a new dataset containing tiny (100 × 100 pixels) and low resolution (96 dpi) vehicle images. The proposed model is then compared with well-known VGG16-based CNN models in terms of accuracy and complexity. Results indicate that although the well-known models provide higher accuracy, the proposed method offers an acceptable accuracy (92.9%) as well as a simple and lightweight solution for vehicle classification in low quality images. Thus, it is believed that this study might provide useful perception and understanding for further research on the use of standard low-cost cameras to enhance the ability of the intelligent systems such as intelligent transportation system applications.

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Vehicle Classification Using Visual Background Extractor and Multi-class Support Vector Machines

Cited by 17 publications

References 8 publications

Convolutional Neural Network Based Vehicle Classification in Adverse Illuminous Conditions for Intelligent Transportation Systems

Convolutional Neural Network Based Vehicle Classification in Adverse Illuminous Conditions for Intelligent Transportation Systems

Bangladeshi Native Vehicle Classification Based on Transfer Learning with Deep Convolutional Neural Network

Deep Learning-Based Vehicle Classification for Low Quality Images

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