Automatic Vehicle type Classification with Convolutional Neural Networks

Roecker, Max N.; Costa, Yandre M. G.; Almeida, João Luiz Ramalheira de; Matsushita, Gustavo H.G.

doi:10.1109/iwssip.2018.8439406

Cited by 26 publications

(25 citation statements)

References 11 publications

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“…The BIT Vehicle Dataset is comprised of 9850 vehicle images with pixel sizes of 1920 × 1080 and 1600 × 1200, which have been captured using two different cameras at different places and time. The BIT Vehicle Dataset consists of six vehicle types, namely, sedan, microbus, SUV, minivan, bus, and truck, and there are 5922, 883, 1392, 476, 558, and 822 images for each corresponding vehicle type [26]. The captured images are varied in terms of view points, surface color of the vehicles, scales, position of the vehicles, and illumination conditions.…”

Section: Image Collectionmentioning

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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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: Image Collectionmentioning

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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“…In recent years, Deep learning and especially Convolutional Neural Networks have been used to tackle the task of vehicle type classification but with different approaches and datasets [6]. CNNs have demonstrated better performance in image classification.…”

Section: Related Workmentioning

confidence: 99%

“…CNNs have demonstrated better performance in image classification. [18,1,6,5] used Convolutional Neural Networks to classify vehicle types. [18] proposed a method based also on a semi-supervised convolutional neural network with Laplacian Filters for kernels.…”

Section: Related Workmentioning

confidence: 99%

“…The first is data augmentation and the second is the CNN model training. In [6], the authors used also the CNNs with low-resolution images from a frontal view. The model achieved an accuracy of 93.90%.…”

Section: Related Workmentioning

confidence: 99%

“…In this work, Machine Learning and Deep Learning techniques were used to classify the views and categories of vehicles. Object classification is a broad field of computer vision and machine learning research that aims to classify objects in images into meaningful categories [6]. This involves predicting the class of a new object in an image according to the information obtained from the training set of data whose category class is known [7].…”

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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