Segmentation and Recognition of Traffic Signs Using Shape Information

Oh, Jeill; Kwak, Hyun-Wook; Sohn, Young-Ho; Kim, Wookhyun

doi:10.1007/11595755_63

Cited by 5 publications

(3 citation statements)

References 5 publications

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“…The work of Fang et al [43] includes using the hue as a color feature and an edge detector method for shape feature extraction. [54] and [55] are other examples of integrating color-based methods with the ones based on shape analysis. Mathias et al et al [56] used Integral Channel Features (ICF) for sign detection which was first established by Dollar et al [57] for the task of pedestrian detection.…”

Section: Detection Based On Hybrid Methodsmentioning

confidence: 99%

Detection and recognition of traffic signs inside the attentional visual field of drivers

Zabihi

Beauchemin

et al. 2017

2017 IEEE Intelligent Vehicles Symposium (IV)

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Traffic sign detection and recognition systems are essential components of Advanced Driver Assistance Systems and self-driving vehicles. In this contribution we present a vision-based framework which detects and recognizes traffic signs inside the attentional visual field of drivers. This technique takes advantage of the driver's 3D absolute gaze point obtained through the combined use of a front-view stereo imaging system and a non-contact 3D gaze tracker. We used a linear Support Vector Machine as a classifier and a Histogram of Oriented Gradient as features for detection. Recognition is performed by using Scale Invariant Feature Transforms and color information. Our technique detects and recognizes signs which are in the field of view of the driver and also provides indication when one or more signs have been missed by the driver.

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Section: Detection Based On Hybrid Methodsmentioning

confidence: 99%

Detection and recognition of traffic signs inside the attentional visual field of drivers

Zabihi

Beauchemin

et al. 2017

2017 IEEE Intelligent Vehicles Symposium (IV)

View full text Add to dashboard Cite

show abstract

“…The current performance of embedded computing systems allows using more sophisticated algorithms of detecting signs in real-time. In addition to color information, all of these systems detect the signs, using the information about the objects' shape [10].…”

Section: Traffic Sign Detectionmentioning

confidence: 99%

Traffic Sign Detection on GPU using Color Shape Regular Expressions

Никоноров¹,

Petrov²,

Yakimov³

2013

Proceedings of the 4th International Workshop on Image Mining. Theory and Applications

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Regular expression matching on GPU is state-of-the-art technique for the processing of a huge amount of the text information, for example, in network activity analyzers and intrusion prevention systems. This paper proposes a fast and robust algorithm for traffic sign detection, which is based on color shape regular expressions matching through the image pixels. We consider a fast massively-parallel GPU implementation of the color shape regular expression, using deterministic finite automaton and special case of non-deterministic finite automaton. The performance of the proposed localization algorithm is compared with the Hough transform, which is commonly used for traffic sign recognition.

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“…, 표지판이 지표면으로부터 수직으로 세워진다는 특징을 이용한 방법 [16] , 웨이블릿 변환을 통해 표지판을 인식하는 방법 [17] , 색상 인식을 통해 교통 표지판의 후보 영역을 추출 한 뒤 SIFT …”

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Real-Time Traffic Information and Road Sign Recognitions of Circumstance on Expressway for Vehicles in C-ITS Environments

Im¹,

Kim²

2017

Journal of the Institute of Electronics and Information Enginee

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Recently, the IoT (Internet of Things) environment is being developed rapidly through network which is linked to intellectual objects. Through the IoT, it is possible for human to intercommunicate with objects and objects to objects. Also, the IoT provides artificial intelligent service mixed with knowledge of situational awareness. One of the industries based on the IoT is a car industry. Nowadays, a self-driving vehicle which is not only fuel-efficient, smooth for traffic, but also puts top priority on eventual safety for humans became the most important conversation topic. Since several years ago, a research on the recognition of the surrounding environment for self-driving vehicles using sensors, lidar, camera, and radar techniques has been progressed actively. Currently, based on the WAVE (Wireless Access in Vehicular Environment), the research is being boosted by forming networking between vehicles, vehicle and infrastructures. In this paper, a research on the recognition of a traffic signs on highway was processed as a part of the awareness of the surrounding environment for self-driving vehicles. Through the traffic signs which have features of fixed standard and installation location, we provided a learning theory and a corresponding results of experiment about the way that a vehicle is aware of traffic signs and additional informations on it.

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Segmentation and Recognition of Traffic Signs Using Shape Information

Cited by 5 publications

References 5 publications

Detection and recognition of traffic signs inside the attentional visual field of drivers

Detection and recognition of traffic signs inside the attentional visual field of drivers

Traffic Sign Detection on GPU using Color Shape Regular Expressions

Real-Time Traffic Information and Road Sign Recognitions of Circumstance on Expressway for Vehicles in C-ITS Environments

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