A real-time traffic sign recognition system

Estable, Stéphane; Schick, J.; Stein, Fridtjof; Janssen, R.; Ott, R.J.; Ritter, Walter; Zheng, Yong-Jian

doi:10.1109/ivs.1994.639506

Cited by 70 publications

(24 citation statements)

References 5 publications

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“…The inner part of the sign is not analyzed. Estable et al [12] developed the fastest system (200 ms). Their hardware consists of four PowerPC's (601) and four transputers (T805).…”

mentioning

confidence: 99%

Road traffic sign detection and classification

Escalera

Moreno

Salichs

et al. 1997

IEEE Trans. Ind. Electron.

481

193

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Abstract-A vision-based vehicle guidance system for road vehicles can have three main roles: 1) road detection; 2) obstacle detection; and 3) sign recognition. The first two have been studied for many years and with many good results, but traffic sign recognition is a less-studied field. Traffic signs provide drivers with very valuable information about the road, in order to make driving safer and easier. We think that traffic signs must play the same role for autonomous vehicles. They are designed to be easily recognized by human drivers mainly because their color and shapes are very different from natural environments. The algorithm described in this paper takes advantage of these features. It has two main parts. The first one, for the detection, uses color thresholding to segment the image and shape analysis to detect the signs. The second one, for the classification, uses a neural network. Some results from natural scenes are shown. On the other hand, the algorithm is valid to detect other kinds of marks that would tell the mobile robot to perform some task at that place.Index Terms-Advanced driver information systems, color/shape processing, computer vision, neural networks, traffic signs recognition.

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“…The inner part of the sign is not analyzed. Estable et al [12] developed the fastest system (200 ms). Their hardware consists of four PowerPC's (601) and four transputers (T805).…”

mentioning

confidence: 99%

Road traffic sign detection and classification

Escalera

Moreno

Salichs

et al. 1997

IEEE Trans. Ind. Electron.

481

193

View full text Add to dashboard Cite

show abstract

“…Road sign detection and classification is still a matter of concern, and is often addressed in Intelligent Vehicles Symposium (see [1], [2], [7], [13], [17], [18], [19], [20]). However, to date, only few works have presented a complete algorithm for the detection and recognition of road signs.…”

Section: Road Sign Detectionmentioning

confidence: 99%

Alerting the drivers about road signs with poor visual saliency

Simon

Tarel²,

Brémond³

2009

2009 IEEE Intelligent Vehicles Symposium

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Abstract-This paper proposes an improvement of Advanced Driver Assistance System based on saliency estimation of road signs. After a road sign detection stage, its saliency is estimated using a SVM learning. A model of visual saliency linking the size of an object and a size-independent saliency is proposed. An eye tracking experiment in context close to driving proves that this computational evaluation of the saliency fits well with human perception, and demonstrates the applicability of the proposed estimator for improved ADAS.

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“…One of the most important and more general works was described by Estable in Ref. [14] about the research by Daimler-Benz on its vehicle VITA-II. As a particular application, a system for the detection of the Stop sign as a blind people support system is presented in Ref.…”

Section: Motivationsmentioning

confidence: 99%

Traffic sign recognition and analysis for intelligent vehicles

Escalera

Armingol

Mata

2003

Image and Vision Computing

385

182

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This paper deals with object recognition in outdoor environments. In this type of environments, lighting conditions cannot be controlled and predicted, objects can be partially occluded, and their position and orientation is not known a priori. The chosen type of objects is traffic or road signs, due to their usefulness for sign maintenance, inventory in highways and cities, Driver Support Systems and Intelligent Autonomous Vehicles. A genetic algorithm is used for the detection step, allowing an invariance localisation to changes in position, scale, rotation, weather conditions, partial occlusion, and the presence of other objects of the same colour. A neural network achieves the classification. The global system not only recognises the traffic sign but also provides information about its condition or state. q

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A real-time traffic sign recognition system

Cited by 70 publications

References 5 publications

Road traffic sign detection and classification

Road traffic sign detection and classification

Alerting the drivers about road signs with poor visual saliency

Traffic sign recognition and analysis for intelligent vehicles

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