Difference between revisions of "Traffic Sign Recognition System (TSRS): SVM and Convolutional Neural Network"

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(Created page with "== Presented by == Egemen Guray == Introduction == == Previous Work == == Motivation == == Model Architecture == == Results == == Conclusion == == References ==")
 
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== Introduction ==
 
== Introduction ==
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In order to increase the sustainability and decrease incidents of autonomous driving, Hasan Nazmul et.al. presents various techniques and models to build a Traffic Sign Recognition System. In this paper Convolutional Neural Network, and Support Vector Machine models are proposed to address this issue.
  
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== Previous Work ==
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Over the years researchers published papers on traffic sign recognition using SVM and CNN. In 2019, Wei-Jong Yang et al proposed a CNN and SVM based TSRS. Using SVM model recognizes the sign and using CNN model predicts which traffic sign it is with a 97% accuracy.
 +
 +
In 2017, Prashengit Dhar et al. proposed a Traffic Sign Recognition system which achieves 97% accuracy with a HSV color model and a CNN classifier.
 +
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In 2019, Aashrith and et al. used CNN to recognize traffic signs. They achieved 99.18% accuracy on Belgium Data and 99.50% accuracy on German Traffic Sign Benchmark (GTSDB).
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In 2019 Pavly Salah Zaki and et al. published a paper on multiple entity detection system which uses combination of F-RCNN and Single Shot Multi Box Detector along with several feature extractors to detect traffic sign on GTSB dataset.
  
== Previous Work ==  
+
== Proposed Methodology ==
 +
 
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For recognizing traffic signs Hasan Nazmul et al. uses two approaches based on CNN and SVM.
 +
They have considered 12 different classes each with 100 images. Dataset was built from cropping video frame which have been collected random videos.
  
== Motivation ==
 
  
 
== Model Architecture ==
 
== Model Architecture ==

Revision as of 19:30, 2 December 2021

Presented by

Egemen Guray

Introduction

In order to increase the sustainability and decrease incidents of autonomous driving, Hasan Nazmul et.al. presents various techniques and models to build a Traffic Sign Recognition System. In this paper Convolutional Neural Network, and Support Vector Machine models are proposed to address this issue.

Previous Work

Over the years researchers published papers on traffic sign recognition using SVM and CNN. In 2019, Wei-Jong Yang et al proposed a CNN and SVM based TSRS. Using SVM model recognizes the sign and using CNN model predicts which traffic sign it is with a 97% accuracy.

In 2017, Prashengit Dhar et al. proposed a Traffic Sign Recognition system which achieves 97% accuracy with a HSV color model and a CNN classifier.

In 2019, Aashrith and et al. used CNN to recognize traffic signs. They achieved 99.18% accuracy on Belgium Data and 99.50% accuracy on German Traffic Sign Benchmark (GTSDB).

In 2019 Pavly Salah Zaki and et al. published a paper on multiple entity detection system which uses combination of F-RCNN and Single Shot Multi Box Detector along with several feature extractors to detect traffic sign on GTSB dataset.

Proposed Methodology

For recognizing traffic signs Hasan Nazmul et al. uses two approaches based on CNN and SVM. They have considered 12 different classes each with 100 images. Dataset was built from cropping video frame which have been collected random videos.


Model Architecture

Results

Conclusion

References