Performance evaluation of support vector machine and convolutional neural network algorithms in real-time vehicle type and color classification

Şentaş, Ali; Tashiev, İsabek; Küçükayvaz, Fatmanur; Kul, Seda; Eken, Süleyman; Sayar, Ahmet; Becerikli, Yaşar

doi:10.1007/s12065-018-0167-z

Cited by 32 publications

(14 citation statements)

References 25 publications

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“…In this research, the proposed ensemble deep learning technique performance is simulated using MATLAB 2019a software with the following system requirements: operating system-Windows 10 (64 bit); processor-Intel core i9; hard disk-3 TB; and RAM-16 GB. In this research, the ensemble deep learning technique performance is validated by compar-ing with a few benchmark techniques such as the GAN-based deep ensemble technique [13], the tiny YOLO with SVM [15], the semisupervised CNN model [19], PCN [21], and the three channels of SF-CNNLS (TC-SF-CNNLS) approach [22]. The primary goal of this research study is to classify the vehicle types from the BIT Vehicle Dataset and MIO-TCD.…”

Section: Resultsmentioning

confidence: 99%

“…Extensive experiments showed that the developed GANs achieved 96.41% precision on MIO-TCD. Additionally, Şentaş et al[15] utilized the Tiny YOLO with the SVM classification technique for vehicle detection and classification. The simulation outcome showed that the developed model obtained 97.9% precision and 99.6% recall on the BIT Vehicle Dataset in vehicle type classification.…”

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confidence: 99%

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Moving Vehicle Detection and Classification Using Gaussian Mixture Model and Ensemble Deep Learning Technique

Jagannathan¹,

Sujatha

Frnda

et al. 2021

Wireless Communications and Mobile Computing

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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 truck). Most of the existing research studies focused only on maximizing the percentage of predictions, which have poor real-time performance and consume more computing resources. To highlight the problems of classifying imbalanced data, a new technique is proposed in this research article for vehicle type classification. Initially, the data are collected from the Beijing Institute of Technology Vehicle Dataset and the MIOvision Traffic Camera Dataset. In addition, adaptive histogram equalization and the Gaussian mixture model are implemented for enhancing the quality of collected vehicle images and to detect vehicles from the denoised images. Then, the Steerable Pyramid Transform and the Weber Local Descriptor are employed to extract the feature vectors from the detected vehicles. Finally, the extracted features are given as the input to an ensemble deep learning technique for vehicle classification. In the simulation phase, the proposed ensemble deep learning technique obtained 99.13% and 99.28% of classification accuracy on the MIOvision Traffic Camera Dataset and the Beijing Institute of Technology Vehicle Dataset. The obtained results are effective compared to the standard existing benchmark techniques on both datasets.

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Section: Resultsmentioning

confidence: 99%

mentioning

confidence: 99%

Moving Vehicle Detection and Classification Using Gaussian Mixture Model and Ensemble Deep Learning Technique

Jagannathan¹,

Sujatha

Frnda

et al. 2021

Wireless Communications and Mobile Computing

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“…Loosely speaking, the SVM has been adopted into various real-world problems [53]- [55], however, not all problems are linearly separable. Though, the SVM algorithm maps the data into higher-dimensional spaces (the feature space) in order to make such non-linear input data linearly separable.…”

Section: B Support Vector Machinesmentioning

confidence: 99%

Android Ransomware Detection Based on a Hybrid Evolutionary Approach in the Context of Highly Imbalanced Data

Almomani

Qaddoura

Habib³

et al. 2021

IEEE Access

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In recent years, Ransomware has been a critical threat that attacks smartphones. Ransomware is a kind of malware that blocks the mobile's system and prevents the user of the infected device from accessing their data until a ransom is paid. Worldwide, Ransomware attacks have led to serious losses for individuals and stakeholders. However, the dramatic increase of Ransomware families makes to the process of identifying them more challenging due to their continuously evolved characteristics. Traditional malware detection methods (e.g., statistical-based prevention methods) fail to combat the evolving Ransomware since they result in a high percentage of false positives. Indeed, developing a non-classical, intelligent technique to safeguarding against Ransomware is of significant importance. This paper introduces a new methodology for the detection of Ransomware that is depending on an evolutionary-based machine learning approach. The binary particle swarm optimization algorithm is utilized for tuning the hyperparameters of the classification algorithm, as well as performing feature selection. The support vector machines (SVM) algorithm is used alongside the synthetic minority oversampling technique (SMOTE) for classification. The utilized dataset is collected from various sources, which consists of 10,153 Android applications, where 500 of them are Ransomware. The performance of the proposed approach SMOTE-tBPSO-SVM achieved merits over traditional machine learning algorithms by having the highest scores in terms of sensitivity, specificity, and g-mean.

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“…Linear Support Vector Machine was used for the classification process, and they reached 91.89% overall accuracy. Şentaş et al [23,24] proposed a vehicle type and color classification method; they classified buses, minivans, microbuses, SUVs, sedans, and trucks using the YOLO classification algorithm and reached 97.90% precision, 99.60% recall, and 89.29% IOU.…”

Section: A Vehicle Classification Based On Type Color and Speed Attributesmentioning

confidence: 99%

Event-Based Microservices With Apache Kafka Streams: A Real-Time Vehicle Detection System Based on Type, Color, and Speed Attributes

et al. 2021

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The work presented in this paper proposes a novel approach to tracking a specific vehicle over the video streams published by the collaborating traffic surveillance cameras. In recent years, smart, effective transportation systems and intelligent traffic management applications are among the topics that have been given importance by various institutions. Developing a scalable, fault-tolerant, and resilient traffic monitoring system that retrieves video chunks with the desired query is challenging. For these challenging problems, stream processing and data retrieval systems have been developed over the years. However, there are still existing shortcomings between users and retrieval systems. This paper investigates the problem of retrieving video chunks by key-value query based on publish/subscribe model. Thus, we propose a hybrid of an asynchronous and synchronous communication mechanism for the Event-Based Microservice framework. We aim to develop generic techniques for better utilization of existing platforms. In the proposed framework, (i) first of all, microservices detect vehicles and extract their type, color, and speed features, and stored them in the metadata repository. (ii) Microservices publish each feature as events (iii) Other microservices selfjoin subscribe to those events, which leads to more events being published by combing all the possibilities: type-color, type-speed, color-speed, and type-color-speed. Finally, (iv) the system visualizes the query result and system status in real-time. When the user has selected color or/and a type or/and a speed feature, the system will return the best-matched vehicles without re-processing the videos. Experimental results show that our proposed system filters messages in real-time and supports easy integration of new microservices with the existing system.

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Performance evaluation of support vector machine and convolutional neural network algorithms in real-time vehicle type and color classification

Cited by 32 publications

References 25 publications

Moving Vehicle Detection and Classification Using Gaussian Mixture Model and Ensemble Deep Learning Technique

Moving Vehicle Detection and Classification Using Gaussian Mixture Model and Ensemble Deep Learning Technique

Android Ransomware Detection Based on a Hybrid Evolutionary Approach in the Context of Highly Imbalanced Data

Event-Based Microservices With Apache Kafka Streams: A Real-Time Vehicle Detection System Based on Type, Color, and Speed Attributes

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