Traffic Sign Classification Using Ring Partitioned Method

Soetedjo, Aryuanto; Yamada, Kôichi

doi:10.1093/ietfec/e88-a.9.2419

Cited by 35 publications

(23 citation statements)

References 7 publications

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“…A new method for traffic sign classification based on grayscale images has been presented in [69]. In the first stage the RGB images were converted to grayscale by using color thresholding and histogramming techniques.…”

Section: Recognition Methodsmentioning

confidence: 99%

Detection and recognition of traffic signs inside the attentional visual field of drivers

Zabihi

Beauchemin

et al. 2017

2017 IEEE Intelligent Vehicles Symposium (IV)

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Traffic sign detection and recognition systems are essential components of Advanced Driver Assistance Systems and self-driving vehicles. In this contribution we present a vision-based framework which detects and recognizes traffic signs inside the attentional visual field of drivers. This technique takes advantage of the driver's 3D absolute gaze point obtained through the combined use of a front-view stereo imaging system and a non-contact 3D gaze tracker. We used a linear Support Vector Machine as a classifier and a Histogram of Oriented Gradient as features for detection. Recognition is performed by using Scale Invariant Feature Transforms and color information. Our technique detects and recognizes signs which are in the field of view of the driver and also provides indication when one or more signs have been missed by the driver.

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Section: Recognition Methodsmentioning

confidence: 99%

Detection and recognition of traffic signs inside the attentional visual field of drivers

Zabihi

Beauchemin

et al. 2017

2017 IEEE Intelligent Vehicles Symposium (IV)

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“…At first, a red color thresholding [9,10] is employed, and a connected component labeling is applied to create blobs layer-1. Using these blobs, the spatial information (bounding boxes) of signs is determined.…”

Section: Blob Trackingmentioning

confidence: 99%

“…Using these blobs, the spatial information (bounding boxes) of signs is determined. Then a blue color thresholding [9,10] is applied to sub-images inside the bounding boxes to create blobs layer-2. Blobs layer-1 and the blobs layer-2 are illustrated in Fig.…”

Section: Blob Trackingmentioning

confidence: 99%

“…The ring-partitioned matching [9] provided a fast and robust matching of images under problems of scale varying, rotation, and partial occlusion. In the ringpartitioned matching, image is partitioned into several ring-shaped areas, and matching between the target and reference images is done by computing the distance or similarity of the normalized histogram of every ring of images.…”

Section: Blob Matchingmentioning

confidence: 99%

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Improving the performance of traffic sign detection using blob tracking

Soetedjo

Yamada

2007

IEICE Electron. Express

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Abstract:We propose a new approach for tracking circular traffic signs from image sequences to improve the performance of traffic sign detection, by reducing search region and suppressing misdetection caused by temporal occlusion or poor quality of image. Our proposed tracking, called two-layered blobs tracking, does not require an accurate model of the fixed object-moving camera system, which is essential in the Kalman-Filter tracking. The experimental results show that the proposed approach could track the circular traffic signs from a moving camera effectively, without any restrictions on speed and movement of the vehicle, and camera installation, thus it is easy to be implemented in real situation. Keywords: traffic sign detection, traffic sign tracking, two-layer blobs, blob tracking, ring-partitioned matching Classification: Science and engineering for electronics References[1] Y. Aoyagi and T. Asakura, "A study on traffic sign recognition in scene image using genetic algorithms and neural networks," Proc.

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“…Some studies present detection and/or classification results around 90% [12,13] or in some cases 95% [10] or more. However, few studies compare with other databases than their own and the used database is seldom made public.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Speed Sign Classification Algorithms Using Shape Based Segmentation of Binary Images

Muhammad

Lavesson

Davidsson

et al. 2009

Computer Analysis of Images and Patterns

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Abstract. Traffic Sign Recognition is a widely studied problem and its dynamic nature calls for the application of a broad range of preprocessing, segmentation, and recognition techniques but few databases are available for evaluation. We have produced a database consisting of 1,300 images captured by a video camera. On this database we have conducted a systematic experimental study. We used four different preprocessing techniques and designed a generic speed sign segmentation algorithm. Then we selected a range of contemporary speed sign classification algorithms using shape based segmented binary images for training and evaluated their results using four metrics, including accuracy and processing speed. The results indicate that Naive Bayes and Random Forest seem particularly well suited for this recognition task. Moreover, we show that two specific preprocessing techniques appear to provide a better basis for concept learning than the others.

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Traffic Sign Classification Using Ring Partitioned Method

Cited by 35 publications

References 7 publications

Detection and recognition of traffic signs inside the attentional visual field of drivers

Detection and recognition of traffic signs inside the attentional visual field of drivers

Improving the performance of traffic sign detection using blob tracking

Analysis of Speed Sign Classification Algorithms Using Shape Based Segmentation of Binary Images

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