On-Road Vehicle Classification Based on Random Neural Network and Bag-of-Visual Words

Hussain, Khaled F.; Moussa, Ghada S.

doi:10.1017/s0269964816000073

Cited by 14 publications

(9 citation statements)

References 28 publications

(34 reference statements)

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“…We use the Iris, Breast Cancer, and Glass datasets from the UCI machine learning repository [86] and the Ovarian cancer dataset [87]. We train the RNN using the procedure described in [88] based on [89]; the related software and data sets are at www.github.com/ASDen/Random Neural Network. The algorithm can be summarized as follows: leftmargin=*,labelsep=4.9mm 1) Assume the given dataset has K pairs of input training patterns for an L − vector x k = (x 1l , ... , x Lk ) associated with the output value L − vector y k = (y 1l , ... , y Lk ).…”

Section: Experimental Results Using the Rnnmentioning

confidence: 99%

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Accurate, Energy-Efficient Classification With Spiking Random Neural Network

Hussain

Bassyouni

Gelenbe

2019

Prob. Eng. Inf. Sci.

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Artificial Neural Network (ANN) based techniques have dominated state-of-the-art results in most problems related to computer vision, audio recognition, and natural language processing in the past few years, resulting in strong industrial adoption from all leading technology companies worldwide. One of the major obstacles that have historically delayed large scale adoption of ANNs is the huge computational and power costs associated with training and testing (deploying) them. In the mean-time, Neuromorphic Computing platforms have recently achieved remarkable performance running more bio-realistic Spiking Neural Networks at high throughput and very low power consumption making them a natural alternative to ANNs. Here, we propose using the Random Neural Network (RNN), a spiking neural network with both theoretical and practical appealing properties, as a general purpose classifier that can match the classification power of ANNs on a number of tasks while enjoying all the features of a spiking neural network. This is demonstrated on a number of real-world classification datasets.

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Section: Experimental Results Using the Rnnmentioning

confidence: 99%

“…Table 1 shows statistics about the used datasets, we use Iris, Breast Cancer, and Glass datasets from the UCI machine learning repository [56] and the Ovarian cancer dataset [64]. We train the RNN using the same procedure described in Hussain and Moussa [49], which can be summarized as:…”

Section: Resultsmentioning

confidence: 99%

Accurate, Energy-Efficient Classification With Spiking Random Neural Network

Hussain

Bassyouni

Gelenbe

2019

Prob. Eng. Inf. Sci.

Self Cite

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“…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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“…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%

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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On-Road Vehicle Classification Based on Random Neural Network and Bag-of-Visual Words

Cited by 14 publications

References 28 publications

Accurate, Energy-Efficient Classification With Spiking Random Neural Network

Accurate, Energy-Efficient Classification With Spiking Random Neural Network

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

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

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