A comprehensive review of vehicle detection using computer vision

Abbas, Aymen Fadhil; Sheikh, Usman Ullah; AL‐Dhief, Fahad Taha; Mohd, Mohd Norzali Haji

doi:10.12928/telkomnika.v19i3.12880

Cited by 32 publications

(21 citation statements)

References 54 publications

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“…Other techniques that have been used, which are image processing and computer vision, video camera has recently been widely employed in traffic monitoring applications, providing essential traffic flow information efficiently. Furthermore, the use of video cameras is supplemented by modern technologies including computer graphics, state-of-the-art cameras, high-end computer functions, and automated video analysis for traffic monitoring [11]. Simulation program is formed in [12] for a smart traffic light system using the laboratory virtual instrument engineering workbench (LabVIEW) It intends to measure the traffic density by investigating the number of vehicles in every lane.…”

Section: Related Workmentioning

confidence: 99%

Integrated tripartite modules for intelligent traffic light system

Kareem

Hoomod

2022

IJECE

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<span>The traffic in urban areas is primarily controlled by traffic lights, contributing to the excessive, if not properly installed, long waiting times for vehicles. The condition is compounded by the increasing number of road accidents involving pedestrians in cities across the world. Thus, this work presents an integrated tripartite module for an intelligent traffic light system. This system has enough ingredients for success that can solve the above challenges. The proposed system has three modules: the intelligent visual monitoring module, intelligent traffic light control module, and the intelligent recommendation module for emergency vehicles. The monitor module is a visual module capable of identifying the conditions of traffic in the streets. The intelligent traffic light control module configures many intersections in a city to improve the flow of vehicles. Finally, the intelligent recommendation module for emergency vehicles offers an optimal path for emergency vehicles. The evaluation of the proposed system has been carried out in Al-Sader city/Bagdad/Iraq. The intelligent recommendation module for the emergency vehicles module shows that the optimization rate average for the optimal path was in range 67.13% to 92%, where the intelligent traffic light control module shows that the optimization ratio was in range 86% to 91.8%.</span>

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Section: Related Workmentioning

confidence: 99%

Integrated tripartite modules for intelligent traffic light system

Kareem

Hoomod

2022

IJECE

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“…Moreover, machine learning (ML) algorithms can present tools and techniques for classifying and distinguishing between two or more classes [12]- [14]. Also, these algorithms have proved their efficiency and effectiveness in different domains such as voice pathology detection [15]- [18], vehicle detection [19], identification of spam emails [20], images classification in the medical domain [21], [22], detection of conflict flows in SDN [23], and language identification [24]- [26]. Furthermore, these algorithms have used efficiently and as a major part in the ear recognition systems [27]- [29].…”

Section: Introductionmentioning

confidence: 99%

Ear recognition system using random forest and histograms of oriented gradients techniques

Mutar

Ahmed

Alsemawi

et al. 2022

IJEECS

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In recent years, systems of ear recognition are considered a significant topic of research in the biometrics field. In such systems, the models of machine learning represent a principal part in order to recognise humans’ identities by using their ear images. In this paper, a system of ear recognition is proposed by using random forest (RF) and histograms of oriented gradients (HOG) techniques. The HOG is used to extract features from ear images. Subsequently, these extracted features will be fed to the RF classifier to classify the ear images with respect to the classes. In this study, the ear images have been selected from the Indian Institute of Technology Delhi, second version (IITD II). The performance of the proposed system has evaluated by using different evaluation measures such as accuracy, specificity, and G-mean. The experimental results show that the proposed system for ear recognition obtains accuracy up to 99.69%. Furthermore, this system archives 99.84% and 80.78% for specificity and G-mean, respectively. The proposed system has the ability to identify persons through their ear images effectively.

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“…Many studies have been conducted in vehicle detection [15], [16]. Some systems are even built to work under challenging conditions, such as dusty weather [17].…”

Section: Introductionmentioning

confidence: 99%

A Solution for Automatic Counting and Differentiate Motorcycles and Modified Motorcycles in Remote Area

Indrabayu¹,

Areni²,

Bustamin³

et al. 2022

IJACSA

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Motorcycles are the most significant contributor to the vehicle numbers in Indonesia, about 81% of all vehicles in the country. In addition, the growth of modified motorcycles has also increased in several areas, particularly remote places. Many studies have been conducted for detecting vehicles. However, most vehicle detection studies were conducted to detect cars or four-wheeled vehicles, and only a few studies were done to detect motorcycles. Further problems increase if the system is implemented in remote areas with limited electricity power resources that need low-cost budget specification computation. This study detects and calculates the number of motor vehicles and modified motorcycles passed on a highway from video data. It proposed Machine Learning instead of Deep Learning to suit the low computational video in remote areas. Computer visionbased methods used in the prediction are optical flow andHistogram Oriented Gradient (HOG) + Support Vector Machine (SVM). Five videos were used in the system testing, taken from the roadsides using a static camera with a resolution of 160x112 pixels at ±135º angle. This research showed that the accuracy of motorcycles and modified motorcycles detection and calculation systems using the HOG + SVM method is higher than the optical flow method. The average accuracy of HOG + SVM for motorcycles and modified motorcycles is 89.70% and 95.16%, respectively.

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A comprehensive review of vehicle detection using computer vision

Cited by 32 publications

References 54 publications

Integrated tripartite modules for intelligent traffic light system

Integrated tripartite modules for intelligent traffic light system

Ear recognition system using random forest and histograms of oriented gradients techniques

A Solution for Automatic Counting and Differentiate Motorcycles and Modified Motorcycles in Remote Area

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