Hybrid of Statistical and Spectral Texture Features for Vehicle Object Classification System

Jayadurga, R.; Gunasundari, R.

doi:10.17485/ijst/2016/v9i27/90832

Cited by 4 publications

(3 citation statements)

References 10 publications

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“…Texture features are used to overcome the disadvantages of color and intensity features. Jayadurga et al [126] enhanced the performance of vehicle classifiers in a highly textured background. A hybrid texture feature extraction, including statistical and spectral texture features, is used without pre-processing for classification.…”

Section: Vision-based Methodsmentioning

confidence: 99%

“…Recurrent neural networks [16], convolutional neural networks (CNN) [31,103], Recurrent Convolutional Neural Networks (R-CNN), deep neural networks [103], Back-Propagation Neural Network (BPN) [126,130], soft radial basis cellular neural network [131], random neural networks (RNNs) [132], Fast Neural Network (FNN) [133], multi-layer perceptron neural network [134], Radial Basis Function (RBF) neural network [135], backpropagation neural networks [126].…”

Section: Neural Network Classification Training Pattern Recognitionmentioning

confidence: 99%

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A Review on Vehicle Classification and Potential Use of Smart Vehicle-Assisted Techniques

Shokravi

Bakhary

et al. 2020

Sensors

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Vehicle classification (VC) is an underlying approach in an intelligent transportation system and is widely used in various applications like the monitoring of traffic flow, automated parking systems, and security enforcement. The existing VC methods generally have a local nature and can classify the vehicles if the target vehicle passes through fixed sensors, passes through the short-range coverage monitoring area, or a hybrid of these methods. Using global positioning system (GPS) can provide reliable global information regarding kinematic characteristics; however, the methods lack information about the physical parameter of vehicles. Furthermore, in the available studies, smartphone or portable GPS apparatuses are used as the source of the extraction vehicle’s kinematic characteristics, which are not dependable for the tracking and classification of vehicles in real time. To deal with the limitation of the available VC methods, potential global methods to identify physical and kinematic characteristics in real time states are investigated. Vehicular Ad Hoc Networks (VANETs) are networks of intelligent interconnected vehicles that can provide traffic parameters such as type, velocity, direction, and position of each vehicle in a real time manner. In this study, VANETs are introduced for VC and their capabilities, which can be used for the above purpose, are presented from the available literature. To the best of the authors’ knowledge, this is the first study that introduces VANETs for VC purposes. Finally, a comparison is conducted that shows that VANETs outperform the conventional techniques.

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Section: Vision-based Methodsmentioning

confidence: 99%

Section: Neural Network Classification Training Pattern Recognitionmentioning

confidence: 99%

A Review on Vehicle Classification and Potential Use of Smart Vehicle-Assisted Techniques

Shokravi

Bakhary

et al. 2020

Sensors

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show abstract

“…Some popular variations of ANN are DNNs, back‐propagation NN (BPN), 7,109 fast NN (FNN), 110 radial basis function (RBF), 111 random NN (RNN), 112 multilayer perceptron (MLP), 62 soft radial basis cellular NN (SRB‐CNN), 113 recurrent NNs, 53 CNNs, 61 and recurrent convolutional NN (R‐CNN) 7 . Each of these networks is slightly different, but the way they work is almost the same.…”

Section: Our Proposed Frameworkmentioning

confidence: 99%

A new comparison framework to survey neural networks‐based vehicle detection and classification approaches

Hashemi

Emami

Sangar

2021

Int J Communication

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Summary The vehicle detection and classification (VDC) problem has received much attention recently due to the increased security threats and the need to develop intelligent transportation systems. A large number of approaches have been proposed for the VDC problem using neural networks. To determine how neural networks‐based approaches have developed for the VDC in recent years, this paper surveys the VDC approaches through a literature review with the range Jan. 2012 through Apr. 2021. To do this, we introduce a new comparison framework to classify and compare the VDC approaches. Our proposed framework is composed of nine comparison dimensions: input data type, vehicle type, scale, scope, dynamicity, vehicle detection method, vehicle classification method, application, and evaluation method. Next, using the proposed framework, we discuss the evolution of the VDC approaches and identify several open issues that have emerged in the field. This paper provides a guide for researchers to use or design robust VDC systems with proper characteristics based on their needs.

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Vehicle Classification in Intelligent Transport Systems: An Overview, Methods and Software Perspective

Gholamhosseinian

Seitz

2021

IEEE Open J. Intell. Transp. Syst.

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Hybrid of Statistical and Spectral Texture Features for Vehicle Object Classification System

Cited by 4 publications

References 10 publications

A Review on Vehicle Classification and Potential Use of Smart Vehicle-Assisted Techniques

A Review on Vehicle Classification and Potential Use of Smart Vehicle-Assisted Techniques

A new comparison framework to survey neural networks‐based vehicle detection and classification approaches

Vehicle Classification in Intelligent Transport Systems: An Overview, Methods and Software Perspective

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