Vehicle counting method based on digital image processing algorithms

Tourani, Ali; Shahbahrami, Asadollah

doi:10.1109/pria.2015.7161621

Cited by 21 publications

(6 citation statements)

References 18 publications

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“…The authors of this paper have contributed to many computer vision and machine learning projects and proposed various approaches in the field of ITS. Some of these approaches include vehicle count using video processing [16], deep learning-based vehicle detection [17], vehicle speed measurement [18][19], license plate localization [8,20], and Farsi character recognition [8]. Accordingly, we claim that we have felt the essence of reliable data for the development of domestic robust applications for Fig.…”

Section: Motivation and Related Workmentioning

confidence: 99%

Iranis: A Large-scale Dataset of Iranian Vehicles License Plate Characters

Tourani

Soroori

Shahbahrami

et al. 2021

2021 5th International Conference on Pattern Recognition and Image Analysis (IPRIA)

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Providing huge amounts of data is a fundamental demand when dealing with Deep Neural Networks (DNNs). Employing these algorithms to solve computer vision problems resulted in the advent of various image datasets to feed the most common visual imagery deep structures, known as Convolutional Neural Networks (CNNs). In this regard, some datasets can be found that contain hundreds or even thousands of images for license plate detection and optical character recognition purposes. However, no publicly available image dataset provides such data for the recognition of Farsi characters used in car license plates. The gap has to be filled due to the numerous advantages of developing accurate deep learning-based systems for law enforcement and surveillance purposes. This paper introduces a large-scale dataset that includes images of numbers and characters used in Iranian car license plates. The dataset, named Iranis, contains more than 83,000 images of Farsi numbers and letters collected from real-world license plate images captured by various cameras. The variety of instances in terms of camera shooting angle, illumination, resolution, and contrast make the dataset a proper choice for training DNNs. Dataset images are manually annotated for object detection and image classification. Finally, and to build a baseline for Farsi character recognition, the paper provides a performance analysis using a YOLO v.3 object detector.

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Section: Motivation and Related Workmentioning

confidence: 99%

Iranis: A Large-scale Dataset of Iranian Vehicles License Plate Characters

Tourani

Soroori

Shahbahrami

et al. 2021

2021 5th International Conference on Pattern Recognition and Image Analysis (IPRIA)

Self Cite

View full text Add to dashboard Cite

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“…Mishra and Banerjee [20] detected vehicle using background, extracted Haar, pyramidal histogram of oriented gradients, shape and scale-invariant feature transform features, designed a multiple kernel classifier based on k-nearest neighbor to divide the vehicles into 4 categories. Tourani and Shahbahrami [21] combined different image/video processing methods including object detection, edge detection, frame differentiation, and Kalman filter to propose a method which resulted in about 95 percent accuracy for classification and about 4 percent error in vehicle detection targets. In these methods, the classification results are very good; however, there are still some problems.…”

Section: Related Workmentioning

confidence: 99%

Pretraining Convolutional Neural Networks for Image-Based Vehicle Classification

Han

Jiang

et al. 2018

Advances in Multimedia

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Vehicle detection and classification are very important for analysis of vehicle behavior in intelligent transportation system, urban computing, etc. In this paper, an approach based on convolutional neural networks (CNNs) has been applied for vehicle classification. In order to achieve a more accurate classification, we removed the unrelated background as much as possible based on a trained object detection model. In addition, an unsupervised pretraining approach has been introduced to better initialize CNNs parameters to enhance the classification performance. Through the data enhancement on manual labeled images, we got 2000 labeled images in each category of motorcycle, transporter, passenger, and others, with 1400 samples for training and 600 samples for testing. Then, we got 17395 unlabeled images for layer-wise unsupervised pretraining convolutional layers. A remarkable accuracy of 93.50% is obtained, demonstrating the high classification potential of our approach.

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“…In practice, vision‐based vehicle detection mainly follows two paradigms [12]. One is motion‐based methods [13–17], which take advantage of time series of videos. The other is appearance‐based methods [18–22], which extract features for detection of images.…”

Section: Introductionmentioning

confidence: 99%

“…However, they are sensitive to noise because much useful information is ignored. Tourani and Shahbahrami [13] proposed a frame difference method by using the edge information and achieved good accuracy in a simple scene. Li et al [14] proposed an adaptive background subtraction method, which can dynamically update the background and choose the optimal threshold to get motion regions.…”

Section: Introductionmentioning

confidence: 99%