Traffic Sign Recognition Based on HOG Feature and SVM

Tang, Jialin; Su, Qinglang; Lin, Chenyu; Wen, Yang; Su, Binghua; Yang, Jun

doi:10.1145/3443467.3443811

Cited by 5 publications

(3 citation statements)

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“…In the research of Tun and Lwin [22], Real-time Myanmar Traffic Sign Recognition System (RMTSRS) was proposed, and each incoming frame was segmented using the color threshold method for traffic sign detection, and the HOG feature was extracted and RMTSRS classified traffic sign types using SVM. In the work of Tang et al [23], the traffic sign was located with Hough transformation based on the spatial characteristics of the image, and the SVM classifier was used to get the training model with HOG features of traffic signs. Tang et al also pointed out that the first thing to recognize traffic signs is to segment the image, to reduce the interference of the image outside the sign area.…”

Section: Related Workmentioning

confidence: 99%

A Novel SVM Network Using HOG Feature for Prohibition Traffic Sign Recognition

Liu

Zhong

2022

Wireless Communications and Mobile Computing

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To recognize prohibition traffic sign, this paper proposes a novel method that is trained by a small number of samples and uses the feature of histogram of oriented gradient (HOG) and support vector machine (SVM) network. The recognition method is mainly divided into three stages. The first stage is image preprocessing, which includes image interception based on ellipse detection, image resizing, and Gamma correction. In the part of image interception, a new ellipse detection method called RHT_MCN is proposed based on RHT, which uses the maximum coincidence number (MCN) of image edge points and detected ellipse edge to choose the final ellipse for image interception. The second stage is the feature extraction of HOG. The third stage is the prohibition traffic sign recognition (PTSR) based on SVM network. In the design and implementation of the PTSR model, a new single-layer SVM network is proposed. The ascending spiral training method of the recognition model is introduced in detail. Finally, the data from GTSRB is used to test and analyze the prohibition traffic sign recognition method. The method is proven to have good applicability.

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Section: Related Workmentioning

confidence: 99%

A Novel SVM Network Using HOG Feature for Prohibition Traffic Sign Recognition

Liu

Zhong

2022

Wireless Communications and Mobile Computing

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“…La tecnología permite mejorar el tiempo de ejecución de las actividades que realiza el hombre, por ello la visión po r computador es un tema actualmente muy estudiado que ha permitido realizar tareas como la detección de rostros, conteo de vehículos, detección de letras y otras actividades, [9] . Para esta investigación se ha revisado bibliografía con referencia a identificación de señales de tránsito y en su mayoría tiene por objetivo mejorar las aplicaciones en vehículos inteligentes [7] , [11] , [25] . La metodología se ha dividido por lo general en dos etapas: la detección y el reconocimiento.…”

Section: Introductionunclassified

“…La segunda etapa es el reconocimiento aplicando algoritmos, algunos con más costo computacional que otros como las redes neuronales [1] , [13] , [23] , matching of Chamfer, algoritmos genéticos, máquinas de vectores soporte [7] , [11] , [14] , y redes neuronales convolucionales (CNN) [2] , [8] , [18] , [24] .…”

Section: Introductionunclassified

Detection and recognition of Peruvian warning traffic signals applied to road inventories-case Eduardo of Habich avenue

Ramos¹,

Flores²,

Valero³

et al. 2022

Proceedings of the 20th LACCEI International Multi-Conference for Engineering, Education and Technology: “Education, Research A

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The purpose of this research is to obtain a database of Peruvian warning traffic signs and propose a tool to automate the road inventory process using image processing algorithms. The database consists of 2026 images of Peruvian warning traffic signs, to detect and recognize them on Av. Eduardo Habich located in Metropolitan Lima, also proposed the following methodology tha t is divided into two parts: The first part consists of collecting da ta in the field for the creation of the database; and the second part consists of the processing of information in the cabinet, where the detection and recognition algorithm for information processing is proposed. The detection stage consists of the use of color and sha p e filters, as well as the performance of two-color models, HSV a nd normalized RGB, for the characteristic yellow color of warning signs. The recognition stage consists of the use of supervised classification tools with the algorithm called support vector machines. Finally, with the development of this research, it was possible to obtain an algorithm that allows the detection of tra ffic signs with a recognition percentage of 62.5% and a solid database that can be fed back and give rise to future research in the automation of traffic signals. road inventories.

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