An efficient real-time speed limit signs recognition based on rotation invariant feature

Liu, Wei; Lv, Jin; Gao, Haihua; Duan, Bobo; Yuan, Huai; Zhao, Hong

doi:10.1109/ivs.2011.5940428

Cited by 15 publications

(12 citation statements)

References 15 publications

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“…The undefined and large number of classes pleads for the use of a hierarchical classification approach as adopted by Liu et al in [10] approach the addition of a new type of sub require retraining their whole cascade of classifiers. For this reason, and also for taking into accoun "text" supplementary signs, we have decided to implement a hand-crafted hierarchical recognition in which we first determine the "meta-class" of the sub "Arrow", "Pictogram", "Text", and " The idea is that for "text" meta-class, interpretation could be done in a further stage by applying character recognition.…”

Section: Recognition Of Traffic Sumentioning

confidence: 99%

“…Nienhüser et al [9] a work handling more types of subei Nässe" ("when raining") d to train a one-against-all Vector Machine (SVM) classifier for each type, which makes difficult to extend their approach for handling Liu et al [10] also include detection (based on gradients and Hough voting) and recognition (using a tree of binary SVMs applied to Fourier and wavelet descriptors) in their system…”

Section: Recognition Of Traffic Sumentioning

confidence: 99%

“…Recognition of supplementary signs is not very different from TSR in general, except for the existence of a potentially very large number of classes, as several sub-sign The undefined and large number use of a hierarchical classification in [10]. However, in their addition of a new type of sub-sign would require retraining their whole cascade of classifiers.…”

Section: Supplementary Signsmentioning

confidence: 99%

See 2 more Smart Citations

Recognition of supplementary signs for correct interpretation of traffic signs

Puthon

Moutarde

Nashashibi

2013

2013 IEEE Intelligent Vehicles Symposium (IV)

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Abstract-Traffic Sign Recognition (TSR) is now relatively well-handled by several approaches. However, traffic signs are often completed by one (or several) supplementary placed below. They are essential for correct interpretation of main sign, as they specify its applicability scope. difficulty of supplementary sub-sign recognition potentially infinite number of classes, as nearly any can be written on them. In this paper, we propose and evaluate a hierarchical approach for recognition of supplementary signs, in which the "meta-class" of the sub-sign (Arrow, P Text or Mixed) is first determined. The classification is based on the pyramid-HOG feature, completed by dark area proportion measured on the same pyramid. large database of images with and without supp shows that the classification accuracy of our approach 95% precision and recall. When used on output of our sub specific detection algorithm, the global correct detection and recognition rate is 91%.

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Section: Recognition Of Traffic Sumentioning

confidence: 99%

Section: Recognition Of Traffic Sumentioning

confidence: 99%

Section: Supplementary Signsmentioning

confidence: 99%

See 1 more Smart Citation

Recognition of supplementary signs for correct interpretation of traffic signs

Puthon

Moutarde

Nashashibi

2013

2013 IEEE Intelligent Vehicles Symposium (IV)

View full text Add to dashboard Cite

show abstract

“…Recognition of supplementary signs is not very different from TSR in general, except for the existence of a potentially very large number of classes, as several sub types consist of "free" text. The undefined and large number of classes pleads for the use of a hierarchical classification approach as adopted by Liu et al in [10] approach the addition of a new type of sub require retraining their whole cascade of classifiers. For this reason, and also for taking into accoun "text" supplementary signs, we have decided to implement a hand-crafted hierarchical recognition in which we first determine the "meta-class" of the sub "Arrow", "Pictogram", "Text", and " The idea is that for "text" meta-class, interpretation could be done in a further stage by applying character recognition.…”

Section: Recognition Of Traffic Sumentioning

confidence: 99%

Recognition of supplementary signs for correct interpretation of traffic signs

Puthon

Moutarde

Nashashibi

2013

2013 IEEE Intelligent Vehicles Symposium Workshops (IV Workshops)

View full text Add to dashboard Cite

Traffic Sign Recognition (TSR) is now relatively well-handled by several approaches. However, traffic signs are often completed by one (or several) supplementary placed below. They are essential for correct interpretation of main sign, as they specify its applicability scope. difficulty of supplementary sub-sign recognition potentially infinite number of classes, as nearly any can be written on them. In this paper, we propose and evaluate a hierarchical approach for recognition of supplementary signs, in which the "meta-class" of the sub-sign (Arrow, P Text or Mixed) is first determined. The classification is based on the pyramid-HOG feature, completed by dark area proportion measured on the same pyramid. large database of images with and without supp shows that the classification accuracy of our approach 95% precision and recall. When used on output of our sub specific detection algorithm, the global correct detection and recognition rate is 91%.

show abstract

“…Performance indexes required by these systems include high recognition rates, real-time implementation, a bank of many traffic sign categories to be used as recognition objects, robustness for variant environments, and feasibility under poor visibility conditions. Brought to you by | Freie Universität Berlin Authenticated Download Date | 6/28/15 1:02 AM Liu et al [12] presented a speed limit sign detection and recognition system. In the detection stage, the computational load is reduced by using a Histogram of Oriented Gradient (HOG) based filter to reduce the error.…”

Section: Relevant Workmentioning

confidence: 99%

Classification with NormalBoost: Case Study Traffic Sign Classification

Fleyeh

Davami

2012

Journal of Intelligent Systems

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NormalBoost is a new boosting algorithm which is capable of classifying a multi-dimensional binary class dataset. It adaptively combines several weak classifiers to form a strong classifier. Unlike many boosting algorithms which have high computation and memory complexities, NormalBoost is capable of classification with low complexity.The purpose of this paper is to present NormalBoost as a framework which establishes a platform to solve classification problems. The approach was tested with a dataset which was extracted automatically from real-world traffic sign images. The dataset contains both images of traffic sign borders and speed limit pictograms. This framework involves the computation of Haar-like features of these images and then employs the NormalBoost classifier to classify these traffic signs. The total number of images which were classified was 6500 binary images. A k-fold validation was invoked to check the validity of the classification which resulted in a classification rate of 98.4% and 98.9% being achieved for these two databases. This framework is distinguished by its invariance to in-plane rotation of the images under consideration. Experiments show that the classification rate remains almost constant when traffic sign images with different angles of rotations were tested.

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An efficient real-time speed limit signs recognition based on rotation invariant feature

Cited by 15 publications

References 15 publications

Recognition of supplementary signs for correct interpretation of traffic signs

Recognition of supplementary signs for correct interpretation of traffic signs

Recognition of supplementary signs for correct interpretation of traffic signs

Classification with NormalBoost: Case Study Traffic Sign Classification

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