A Decision Fusion and Reasoning Module for a Traffic Sign Recognition System

Meuter, Mirko; Nunn, Christian; Gormer, Steffen; Müller-Schneiders, Stefan; Kummert, Anton

doi:10.1109/tits.2011.2157497

Cited by 53 publications

(15 citation statements)

References 17 publications

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“…Using the Canny edge detection or other variations, the edges can be detected [78], [79], [80], [81]. The histogram of oriented gradients (HOG) method was also proposed.…”

Section: B Shape Featuresmentioning

confidence: 99%

Overview of Environment Perception for Intelligent Vehicles

Zhu

Yuen

Mihaylova

et al. 2017

IEEE Trans. Intell. Transport. Syst.

294

125

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Abstract-This paper presents a comprehensive literature review on environment perception for intelligent vehicles. The state-of-the-art algorithms and modeling methods for intelligent vehicles are given, with a summary of their pros and cons. A special attention is paid to methods for lane and road detection, traffic sign recognition, vehicle tracking, behavior analysis, and scene understanding. In addition, we provide information about datasets, common performance analysis, and perspectives on future research directions in this area.Index Terms-Intelligent vehicles, environment perception and modeling, lane and road detection, traffic sign recognition, vehicle tracking and behavior analysis, scene understanding.

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“…Using the Canny edge detection or other variations, the edges can be detected [78], [79], [80], [81]. The histogram of oriented gradients (HOG) method was also proposed.…”

Section: B Shape Featuresmentioning

confidence: 99%

Overview of Environment Perception for Intelligent Vehicles

Zhu

Yuen

Mihaylova

et al. 2017

IEEE Trans. Intell. Transport. Syst.

294

125

View full text Add to dashboard Cite

show abstract

“…The most popular feature is edges: edges can be directly obtained from the raw picture or from pre-segmented images. Edges are used as the only feature in [4], [17] and [18]. Although edges comprise the most popular feature choice, there are other options.…”

Section: Road Sign Recognitionmentioning

confidence: 99%

Exploiting Visual Saliency and Bag-of-Words for Road Sign Recognition

Wei

Tang

et al. 2014

IEICE Trans. Inf. & Syst.

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SUMMARYIn this paper, we propose a novel method for road sign detection and recognition in complex scene real world images. Our algorithm consists of four basic steps. First, we employ a regional contrast based bottom-up visual saliency method to highlight the traffic sign regions, which usually have dominant color contrast against the background. Second, each type of traffic sign has special color distribution, which can be explored by top-down visual saliency to enhance the detection precision and to classify traffic signs into different categories. A bag-of-words (BoW) model and a color name descriptor are employed to compute the specialclass distribution. Third, the candidate road sign blobs are extracted from the final saliency map, which are generated by combining the bottom-up and the top-down saliency maps. Last, the color and shape cues are fused in the BoW model to express blobs, and a support vector machine is employed to recognize road signs. Experiments on real world images show a high success rate and a low false hit rate and demonstrate that the proposed framework is applicable to prohibition, warning and obligation signs. Additionally, our method can be applied to achromatic signs without extra processing.

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“…To this end, various methods for traffic sign recognition have been proposed, where several representative approaches are detailed in references [2,3,5,7,8,10,11,15]. A common element of the published works that addressed the traffic sign recognition is that they were based on specific constraints in relation to the color and shape of the road signs being evaluated.…”

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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A Decision Fusion and Reasoning Module for a Traffic Sign Recognition System

Cited by 53 publications

References 17 publications

Overview of Environment Perception for Intelligent Vehicles

Overview of Environment Perception for Intelligent Vehicles

Exploiting Visual Saliency and Bag-of-Words for Road Sign Recognition

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

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