Traffic signs localisation for highways inventory from a video camera on board a moving collection van

Arnoul, P.; Viala, Marc; Guerin, Jean-Pierre; Mergy, M.

doi:10.1109/ivs.1996.566368

Cited by 23 publications

(16 citation statements)

References 4 publications

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“…To solve the problems about background color, researchers have developed numerous studies. Some researchers studied in the RGB space [22][23][24][25], while other researchers prefer to study HIS space [26][27][28][29] for it is more immune to lighting changes. Also, Luv space has been studied as well [30].…”

Section: Topical Problems and Solutionsmentioning

confidence: 99%

Research Progress of Machine Vision Technology in Artificial Intelligence

Zhao¹

2017

dtcse

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With the development of computer science, artificial intelligence is gradually applied into all aspects of human life. A new discipline is produced in artificial intelligence image processing, that is, machine vision. Machine vision is the forefront of interdisciplinary research, and it is different from the study of human or animal vision, which is to use geometric, physical and learning techniques to build the model, using statistical methods to deal with data. Machine vision refers to the computer achieving the human visual function of perception, recognition and understanding in the objective world of three-dimensional scene. This paper introduces the research content and the development history of machine vision, and it summarizes the related application research of machine vision. Finally, we put forward the existing problems and give the direction of future research.

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Section: Topical Problems and Solutionsmentioning

confidence: 99%

Research Progress of Machine Vision Technology in Artificial Intelligence

Zhao¹

2017

dtcse

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“…Although other spaces are used, for example, the YUV system is considered in [6] to detect blue rectangular signs, the most frequently employed space is the hue saturation intensity (HSI) system because color information, which is encoded with hue and saturation components, presents low variations for objects of interest with a similar color. In [7], proper thresholds on hue and saturation bands are fixed to extract the red and blue colors. In [8], a nonlinear transformation over hue and saturation is employed to enhance the desired colors in the image (red and blue) using two lookup tables for every color for which we are looking.…”

Section: State Of the Artmentioning

confidence: 99%

Road-Sign Detection and Recognition Based on Support Vector Machines

Maldonado-Bascón

Lafuente-Arroyo

Gil-Jiménez

et al. 2007

IEEE Trans. Intell. Transport. Syst.

642

264

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Abstract-This paper presents an automatic road-sign detection and recognition system based on support vector machines (SVMs). In automatic traffic-sign maintenance and in a visual driverassistance system, road-sign detection and recognition are two of the most important functions. Our system is able to detect and recognize circular, rectangular, triangular, and octagonal signs and, hence, covers all existing Spanish traffic-sign shapes. Road signs provide drivers important information and help them to drive more safely and more easily by guiding and warning them and thus regulating their actions. The proposed recognition system is based on the generalization properties of SVMs. The system consists of three stages: 1) segmentation according to the color of the pixel; 2) traffic-sign detection by shape classification using linear SVMs; and 3) content recognition based on Gaussian-kernel SVMs. Because of the used segmentation stage by red, blue, yellow, white, or combinations of these colors, all traffic signs can be detected, and some of them can be detected by several colors. Results show a high success rate and a very low amount of false positives in the final recognition stage. From these results, we can conclude that the proposed algorithm is invariant to translation, rotation, scale, and, in many situations, even to partial occlusions.

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“…It is a tedious task because the signs appear from time to time, and because the operator has to pay great attention. The Esprit European project AUTOCAT [4] presents a van developed for the automatic gathering of the traffic sign position. 2.…”

Section: Motivationsmentioning

confidence: 99%

“…Other researchers prefer working with spaces more immune to lighting changes. Although the HSI space is the most used [4,21,25,34], the Luv space has been also used [24].…”

Section: Sign Detectionmentioning

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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Traffic signs localisation for highways inventory from a video camera on board a moving collection van

Cited by 23 publications

References 4 publications

Research Progress of Machine Vision Technology in Artificial Intelligence

Research Progress of Machine Vision Technology in Artificial Intelligence

Road-Sign Detection and Recognition Based on Support Vector Machines

Traffic sign recognition and analysis for intelligent vehicles

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