An approach towards efficient detection and recognition of traffic signs in videos using neural networks

H.S.G, Supreeth; Patil, Chandrashekar M

doi:10.1109/wispnet.2016.7566175

Cited by 10 publications

(7 citation statements)

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“…A detection rate of 93.3% in daylight and 95% in shadow for color segmentation with a recognition rate of 100% and 97% in daylight and shadow respectively was observed in Ref. [28] where Shape Size Constraints and Auto-Associative Neural Networks were the methods used. Korean TSD is a dataset where training was done with only Positive samples i.e.…”

Section: Review Based On Frameworkmentioning

confidence: 89%

“…The most widely used supervised learning approaches for traffic sign recognition are ANN Ref. [6,25,26,28,36,38,40],SVM Ref. [12,21,23], Random Forest Ref.…”

Section: Analysis Aspects Of Traffic Sign Detection and Recognitionmentioning

confidence: 99%

“…In Ref. [28] a hybrid method utilizing the combination of RGB color space with shape and size constraint analysis based segmentation is used in detection phase along with auto-associative neural network for sign identification. This framework produces highly accurate results only in static images.…”

Section: Review Based On Frameworkmentioning

confidence: 99%

See 2 more Smart Citations

Traffic Sign Detection based on Color Segmentation of Obscure Image Candidates: A Comprehensive Study

Nandi¹,

Saif²,

Paul³

et al. 2018

IJMECS

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Automated Vehicular System has become a necessity in the current technological revolution. Real Traffic sign detection and recognition is a vital part of that system that will find roadside traffic signs to warn the automated system or driver beforehand of the physical conditions of roads. Mostly, researchers based on Traffic sign detection face problems such as locating the sign, classifying it and distinguishing one sign from another. The most common approach for locating and detecting traffic signs is the color information extraction method. The accuracy of color information extraction is dependent upon the selection of a proper color space and its capability to be robust enough to provide color analysis data. Techniques ranging from template matching to critical Machine Learning algorithms are used in the recognition process. The main purpose of this research is to give a review based on methods and framework of Traffic Sign Detection and Recognition solution and discuss also the current challenges of the whole solution.

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Section: Review Based On Frameworkmentioning

confidence: 89%

“…The most widely used supervised learning approaches for traffic sign recognition are ANN Ref. [6,25,26,28,36,38,40],SVM Ref. [12,21,23], Random Forest Ref.…”

Section: Analysis Aspects Of Traffic Sign Detection and Recognitionmentioning

confidence: 99%

See 1 more Smart Citation

Traffic Sign Detection based on Color Segmentation of Obscure Image Candidates: A Comprehensive Study

Nandi¹,

Saif²,

Paul³

et al. 2018

IJMECS

View full text Add to dashboard Cite

show abstract

“…Markus et al [22] uses modern variants of HOG features for detection and sparse representations for classification and Gangyi et al [23] presents the method that uses the HOG and a coarse-to-fine sliding window scheme for the detection and recognition of traffic signs, respectively. Supreeth et al [24] presents color and shape based detection scheme aimed at detection of red color traffic signs that are recognized using the auto associative neural networks. Nadra Ben et al [25] presents a traffic sign detection and recognition scheme aimed at recognition and tracking of the prohibitory signs.…”

Section: Road Signs Detection and Recognitionmentioning

confidence: 99%

“…Similarly, the constraints on the size of the digit candidates in circular speed limit signs are as per Eqs. (24) and (25). Considering the fact that rectangular speed limit signs consist two-digits alongside the characters, it must be ensured that the selected candidates are of digits of speed limit sign and not the characters.…”

Section: F Digit Segmentationmentioning

confidence: 99%