A Road Traffic Signal Recognition System Based on Template Matching Employing Tree Classifier

Varun, S.; Singh, Surendra Pal; Kunte, R. Sanjeev; Samuel, R. D. Sudhaker; Philip, Bindu

doi:10.1109/iccima.2007.190

Cited by 24 publications

(14 citation statements)

References 3 publications

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“…50 Color-based approaches implement thresholding or segmentation techniques based on a priori knowledge of the intended color appearance of the road signs. [51][52][53][54][55][56][57] More recently, advances in application of machine learning techniques to color segmentation for road sign localization have been reported. For example, Ref.…”

Section: Sensing For Other Adaptive and Warningmentioning

confidence: 99%

Computer vision in roadway transportation systems: a survey

Loce

Bernal

et al. 2013

J. Electron. Imaging

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Abstract. There is a worldwide effort to apply 21st century intelli-gence to evolving our transportation networks. The goals of smart transportation networks are quite noble and manifold, including safety, efficiency, law enforcement, energy conservation, and emis-sion reduction. Computer vision is playing a key role in this transpor-tation evolution. Video imaging scientists are providing intelligent sensing and processing technologies for a wide variety of applications and services. There are many interesting technical challenges includ-ing imaging under a variety of environmental and illumination condi-tions, data overload, recognition and tracking of objects at high speed, distributed network sensing and processing, energy sources, as well as legal concerns. This paper presents a survey of computer vision techniques related to three key problems in the transportation domain: safety, efficiency, and security and law enforcement. A broad review of the literature is complemented by detailed treatment of a few selected algorithms and systems that the authors believe represent the state-of-the-art. © The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI. [DOI: 10.1117/1.JEI.22.4.041121]

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Section: Sensing For Other Adaptive and Warningmentioning

confidence: 99%

Computer vision in roadway transportation systems: a survey

Loce

Bernal

et al. 2013

J. Electron. Imaging

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“…Hoferlin's work is limited to recognizing only circular signs, which in most parts of the world are used to enforce speed limits most of the time. Varan et al [22] present a template matching based automated tra c sign recognition system generating invariant features for translation, scale, rotation, weather conditions, and partial occlusion. They provide promising results, but the template matching procedure is computationally expensive.…”

Section: Related Workmentioning

confidence: 99%

Recognizing traffic signs using flexible Discrete Cosine Transform (DCT) grid

Noon¹,

Javed

Mannan³

et al. 2017

Scientia Iranica

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“…The idea is that the areas corresponding to traffic signs of target color will survive the segmentation process. The segmentation is sometimes performed in RGB color space [11], [12], [13], [14], although it often fails because RGB color space is sensitive to illumination changes. The prevalent approach is to segment the image in HSI color space [5], [6], [8], [15], [16], which should eliminate problems with illumination to some extent.…”

Section: Related Workmentioning

confidence: 99%

A computer vision assisted geoinformation inventory for traffic infrastructure

Šegvić

Brkić

Kalafatić

et al. 2010

13th International IEEE Conference on Intelligent Transportation Systems

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Geoinformation inventories are often employed as a tool for providing a comprehensive view onto the required state of traffic control infrastructure. They are especially important in road safety inspection where, in combination with georeferenced video, they enable repeatable off-line and off-site assessments as an attractive aternative to classic onsite inspection. Nevertheless, manual assessments are tedious and time-consuming even when performed off-line, and this seriously impairs the potential of the geoinformation inventory concept. This paper therefore researches a hypothesis that suitable georeferenced video processing techniques would allow reliable automation of the following operations: i) creation of the traffic inventory from the given video, and ii) assessing the video against the state in the inventory. Prominent computer vision approaches have been rigorously and systematically evaluated and the obtained results are presented. The results seem to support the hypothesis, although further work is required for a more definite answer.

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A Road Traffic Signal Recognition System Based on Template Matching Employing Tree Classifier

Cited by 24 publications

References 3 publications

Computer vision in roadway transportation systems: a survey

Computer vision in roadway transportation systems: a survey

Recognizing traffic signs using flexible Discrete Cosine Transform (DCT) grid

A computer vision assisted geoinformation inventory for traffic infrastructure

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