An optimization on pictogram identification for the road-sign recognition task using SVMs

Bascón, Saturnino Maldonado; Rodríguez, Jorge; Arroyo, Sergio Lafuente; Caballero, Antonio; López-Ferreras, F.

doi:10.1016/j.cviu.2009.12.002

Cited by 106 publications

(30 citation statements)

References 32 publications

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“…Piccioli of Italy, proposed a template matching method based on geometric information [9] , which had a good recognition rate, but the speed is slow. Fang Ze ping of Beijing University of Technology, using a template matching method [10] based on color feature.…”

Section: ) Matching Based On Feature Classmentioning

confidence: 99%

A Survey of Traffic Sign Recognition

Zhong¹,

Yu²,

Zhang³

2015

Proceedings of the International Conference on Logistics, Engineering, Management and Computer Science

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Abstract-As the main component of intelligent transportation systems, Traffic sign recognition system has been extensively studied in intelligent vehicles, military and unmanned vehicles and other projects. In this paper, firstly, it briefly introduced the traffic sign recognition research status and development background, and then according to different recognition review,it mainstreamd traffic sign recognition method, through comparative analysis of various methods,it summarized the in traffic sign recognition, and prospected the future direction of development.

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Section: ) Matching Based On Feature Classmentioning

confidence: 99%

A Survey of Traffic Sign Recognition

Zhong¹,

Yu²,

Zhang³

2015

Proceedings of the International Conference on Logistics, Engineering, Management and Computer Science

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“…Additional problems are generated by background objects that resemble the color characteristics of particular traffic signs. This challenging road environment complicates the traffic sign detection and several algorithms have been proposed to address this problem [1,2,3,4,5,7,8,10,11]. Most of the developed TSR algorithms employed specific features relating to the shape and color that are extracted from different categories of road signs.…”

Section: Introductionmentioning

confidence: 99%

Two algorithms for detection of mutually occluding traffic signs

Bui-Minh

Ghita

Whelan

et al. 2012

2012 International Conference on Control, Automation and Information Sciences (ICCAIS)

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Abstract-The robust identification of the traffic signs represents the first and one of the most important steps in the development of a traffic sign recognition (TSR) system. Traffic signs detection usually involves a color segmentation process that uses the information related to the chromatic properties of the road signs. Since the traffic video data is captured in diverse road and weather conditions, the problem relating to traffic sign detection is quite challenging. Among several issues that need to be addressed during this processing stage, the problem generated by mutually occluding traffic signs (mutual occlusion occurs when one traffic sign partially occludes the surface of other road signs) that are attached to the same pole require special attention. In these situations the color segmentation process fails to correctly identify the regions that are associated with the traffic signs. These traffic sign detection failures compromise the performance of other stages of the TSR system and in this paper we propose two approaches that address the segmentation of mutually occluding traffic signs. The first approach uses the information associated with the inner parts of the traffic signs, while the second approach applies the watershed transform to identify the signs that have their borders in contact or are mutually occluding.

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“…The experiments were performed using a relative small subset of traffic signs with triangular and circular shapes and results were provided to illustrate the response of the classifier to each type of the traffic sign. A related approach was proposed by MaldonadoBascón et al [2,4] where in the first step a color segmentation algorithm was applied to extract the road sign candidates using linear SVMs, while in the second stage the candidate regions where classified using Gaussian-kernel SVMs.…”

Section: Introductionmentioning

confidence: 99%

A robust algorithm for detection and classification of traffic signs in video data

Bui-Minh

Ghita

Whelan

et al. 2012

2012 International Conference on Control, Automation and Information Sciences (ICCAIS)

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Abstract-The accurate identification and recognition of the traffic signs is a challenging problem as the developed systems have to address a large number of imaging problems such as motion artifacts, various weather conditions, shadows and partial occlusion, issues that are often encountered in video traffic sequences that are captured from a moving vehicle. These factors substantially degrade the performance of the existing traffic sign recognition (TSR) systems and in this paper we detail the implementation of a new strategy that entails three distinct computational stages. The first component addresses the robust identification of the candidate traffic signs in each frame of the video sequence. The second component discards the traffic sign candidates that do not comply with stringent shape constraints, and the last component implements the classification of the traffic signs using Support Vector Machines (SVMs). The main novel elements of our TSR algorithm are given by the approach that has been developed for traffic sign classification and by the experimental evaluation that was employed to identify the optimal image attributes that are able to maximize the traffic sign classification performance. The TSR algorithm has been validated using video sequences that include the most important categories of signs that are used to regulate the traffic on the Irish and UK roads, and it achieved 87.6% sign detection, 99.2% traffic sign classification accuracy and 86.7% overall traffic sign recognition.

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An optimization on pictogram identification for the road-sign recognition task using SVMs

Cited by 106 publications

References 32 publications

A Survey of Traffic Sign Recognition

A Survey of Traffic Sign Recognition

Two algorithms for detection of mutually occluding traffic signs

A robust algorithm for detection and classification of traffic signs in video data

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