Front and Rear Vehicle Classification

Baghdadi, Sara; Aboutabit, Noureddine

doi:10.1007/978-3-030-36674-2_28

Cited by 3 publications

(7 citation statements)

References 15 publications

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“…Unfortunately, there is no previous work on vehicle view classification topic except our last work [2]. We developed a classification system of the vehicle's front and back using two descriptors HOG and LBP with two classifiers SVM and kNN.…”

Section: Related Workmentioning

confidence: 99%

“…Several factors make the recognition of rear and front views very difficult, including the similarity of shape, size, and color [2]. In this step, we used Machine Learning techniques including traditional algorithms and Convolutional Neural Networks.…”

Section: A Vehicle View Classificationmentioning

confidence: 99%

“…Cells are grouped in blocks and then all vectors of all blocks are combined in series to obtain the final feature vector for classification [22]. ( , ) = � ( , ) 2 + ( , ) 2 (1)…”

Section: ) Histogram Of Oriented Gradients (Hog)mentioning

confidence: 99%

“…Actually, with the increasing number of vehicles and surveillance cameras, it is necessary to automate vehicle recognition to collect traffic data [1]. Automatic vehicle recognition is an integral part of Intelligent Transportation Systems and has become an important subject of study for monitoring and surveillance issues [2]. It presents a particularly challenging problem in contemporary road safety.…”

Section: Introductionmentioning

confidence: 99%

“…Second, most researchers have used just one, two or three methods in their comparative studies. Third, there is not a lot of work being done on vehicle view recognition [2]. Forth, no one has previously developed a view-independent classification system for vehicle categories.…”

Section: Introductionmentioning

confidence: 99%

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View-independent Vehicle Category Classification System

Baghdadi¹,

Aboutabit²

2021

IJACSA

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Vehicle category classification is important, but it is a challenging task, especially, when the vehicles are captured from a surveillance camera with different view angles. This paper aims to develop a view-independent vehicle category classification system. It proposes a two-phase system: one phase recognizes the view angles helping the second phase to recognize the vehicle category including bus, car, motorcycle, and truck. In each phase, several descriptors and Machine Learning techniques including traditional algorithms and Deep neural networks are employed. In particular, we used three descriptors: HOG (Histogram of Oriented Gradient), LBP (Local Binary Patterns) and Gabor filter with two classifiers SVM (Support Vector Machine) and k-NN (k-Nearest Neighbor). And also, we used the Convolutional Neural Network (CNN, or ConvNet). Three experiments have been elaborated based on many datasets. The first experiment is dedicated to choosing the best approach for the recognition of views: rear or front. The second experiment aims to classify the vehicle categories based on each view. In the third experiment, we developed the overall system, the categories were classified independently of the view. Experimental results reveal that CNN gives the highest recognition accuracy of 94.29% in the first experiment, and HOG with SVM or k-NN gives the best results (99.58%, 99.17%) in the second experiment. The system can robustly recognize vehicle categories with an accuracy of 95.77%.

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Section: Related Workmentioning

confidence: 99%

Section: A Vehicle View Classificationmentioning

confidence: 99%

“…Cells are grouped in blocks and then all vectors of all blocks are combined in series to obtain the final feature vector for classification [22]. ( , ) = � ( , ) 2 + ( , ) 2 (1)…”

Section: ) Histogram Of Oriented Gradients (Hog)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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View-independent Vehicle Category Classification System

Baghdadi¹,

Aboutabit²

2021

IJACSA

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Transfer Learning for classifying front and rear views of vehicles

Baghdadi

Aboutabit

2021

J. Phys.: Conf. Ser.

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Various computer systems have been proposed to classify vehicles according to several criteria (category, brand, model). Unfortunately, there is not much research on the classification of views, especially front and rear views. Several factors make this classification very difficult including similarity in shape, size, and color. This work aims to classify front and rear views of vehicles using the Transfer Learning (TL) approach. Here, we used a pre-trained CNN (AlexNet) that has been trained on more than a million images and can classify images into 1000 object categories. Thus, we transferred its learned knowledge and applied it to our new task (Classifying vehicle views). We conducted then two experiments. The first experiment has two scenarios: the first scenario is devoted to Transfer Learning using the AlexNet model, and the second scenario aims to build a network from scratch inspired from AlexNet. Experimental results reveal that the Transfer Learning approach gives high results. On the other hand, in the second experiment, we decided to use TL-AlexNet to extract features and train them with an SVM classifier instead of fully connected layers. And also, we combined the SVM with the fully connected layers. The accuracy rates have been improved after this experiment.

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Front and Rear Classification of Vehicles in Indian Context using Deep Neural Networks

Rath

Swain

2023

2023 International Conference on Communication, Circuits, and Systems (IC3S)

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Front and Rear Vehicle Classification

Cited by 3 publications

References 15 publications

View-independent Vehicle Category Classification System

View-independent Vehicle Category Classification System

Transfer Learning for classifying front and rear views of vehicles

Front and Rear Classification of Vehicles in Indian Context using Deep Neural Networks

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