A Comparative Study of Vehicle Detection Methods

Baghdadi, Sara; Aboutabit, Noureddine

doi:10.1007/978-3-030-11928-7_83

Cited by 3 publications

(7 citation statements)

References 5 publications

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“…The HOG directions are compiled for each pixel of these cells using gradient extraction operator. A histogram is formed in different orientations according to the accumulated value of the gradient, the HOG features of each cell are extracted to the series, and a one-dimensional feature vector of the vehicle image is obtained [4].…”

Section: A Descriptors 1) Histogram Of Oriented Gradient Hog Featuresmentioning

confidence: 99%

“…SURF descriptors have been used to locate and recognize objects, people or faces, to track objects and to extract points of interest [4].…”

Section: ) Speeded Up Robust Features (Surf)mentioning

confidence: 99%

“…The interval between y = 1or y = -1 and y = 0 is the geometrical margin. In order to minimize errors, it is necessary to maximize the geometrical margin [4].…”

Section: ) Speeded Up Robust Features (Surf)mentioning

confidence: 99%

“…Then it selects k training samples which are nearest to the testing sample x and counts the category of the k training samples selected. The testing sample belongs to the category which has the largest number of votes in the same category [4], [26].…”

Section: ) Speeded Up Robust Features (Surf)mentioning

confidence: 99%

“…Every vehicle must not exceed this line in order to avoid passing to the opposite direction of the traffic. Thus, prohibited overtaking is especially a hard problem that still remains a challenging task [4].…”

Section: Introductionmentioning

confidence: 99%

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Illumination Correction in a Comparative Analysis of Feature selection for Rear-View Vehicle Detection

Baghdadi¹,

Aboutabit²

2019

IJMLC

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One of the most frequent infractions on the road is the act of a vehicle crossing to the wrong side of the road to pass another vehicle traveling in the same direction. Automatic detection of this violation can be a challenging issue. Thus, we aim to develop a computer vision system to robustly detect forbidden overtaking observed from a fixed camera. Our approach is based on two main phases: Line Detection and Vehicle Detection. In this work, we focus on the vehicle detection stage. Here, features are extracted from the image and then classified using machine learning algorithms. In a first experiment, we constructed different models using features such as HOG (Histogram of Oriented Gradient), SURF (Speeded up Robust Features), Gabor filter and LBP (Local Binary Patterns), and machine learning classifiers as SVM (Support Vector Machines), kNN (k-Nearest Neighbor) and Decision Tree. Then, we merged the best descriptors to combine the advantages of their different robustness in order to build a strong vehicle detection model. The performances of all constructed models are evaluated on the GTI database. In a second experiment, illumination normalization techniques were applied to image database for the effective models. Thus, the first experimental results reveal that the combination HOG+ LBP+ Gabor Phase performs well with SVM. The second experimental results show that the Variational Retinex algorithm-based illumination correction provides a significant improvement in the detection rate.

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Section: A Descriptors 1) Histogram Of Oriented Gradient Hog Featuresmentioning

confidence: 99%

“…SURF descriptors have been used to locate and recognize objects, people or faces, to track objects and to extract points of interest [4].…”

Section: ) Speeded Up Robust Features (Surf)mentioning

confidence: 99%

“…The interval between y = 1or y = -1 and y = 0 is the geometrical margin. In order to minimize errors, it is necessary to maximize the geometrical margin [4].…”

Section: ) Speeded Up Robust Features (Surf)mentioning

confidence: 99%

Section: ) Speeded Up Robust Features (Surf)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Illumination Correction in a Comparative Analysis of Feature selection for Rear-View Vehicle Detection

Baghdadi¹,

Aboutabit²

2019

IJMLC

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Front and Rear Vehicle Classification

Baghdadi

Aboutabit

2020

Advances in Intelligent Systems and Computing

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A Novel YOLO-ARIA Approach for Real-Time Vehicle Detection and Classification in Urban Traffic

2024

IJIES

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The number of vehicles traveling between cities has increased significantly with the acceleration of urbanization. This has resulted in several traffic-related issues, including traffic congestion and the need to collect information on the number and variety of vehicles on the road. In this study, we propose you only look once (YOLO) artificial real-time intelligent analysis (ARIA) based intelligent traffic monitoring system. YOLO is an algorithm that is capable of detecting objects in images and recordings. We improved YOLO's feature extraction capabilities to improve its vehicle detection accuracy. In addition, we proposed detection models with C3X (convolution neural network) module in the backbone of YOLO. In our experiments, the proposed system attained 99,1% accuracy with 98,3% precision and 98,4% recall on datasets obtained from the CCTV monitoring portal of the semarang city government. In addition, the proposed system has a higher average precision than other vehicle detection and classification methods. Considering the current environmental conditions, the proposed system can classify vehicles in real-time. This makes it a valuable planning and traffic-management instrument.

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A Comparative Study of Vehicle Detection Methods

Cited by 3 publications

References 5 publications

Illumination Correction in a Comparative Analysis of Feature selection for Rear-View Vehicle Detection

Illumination Correction in a Comparative Analysis of Feature selection for Rear-View Vehicle Detection

Front and Rear Vehicle Classification

A Novel YOLO-ARIA Approach for Real-Time Vehicle Detection and Classification in Urban Traffic

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