A Vision based Indian Traffic Sign Classification
Altaf Alam1, Zainul Abdin Jaffery2

1Altaf Alam*, Department of electrical engineering, Jamia Millia Islamia, New Delhi, India
2Zainul Abdin Jaffery, Department of electrical engineering, Jamia Millia Islamia, New Delhi, India
Manuscript received on March 03, 2020. | Revised Manuscript received on March 16, 2020. | Manuscript published on March 30, 2020. | PP: 3132-3141 | Volume-8 Issue-6, March 2020. | Retrieval Number: F8775038620 /2020©BEIESP | DOI: 10.35940/ijrte.F8775.038620

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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: In this paper, an algorithm is proposed to classify the Indian traffic sign as mandatory cautionary and informatory class. In order to complete the task, system extracted the speed up robust features (SURF) from the Indian traffic sign data, and exploited these features to train support vector machine (SVM) algorithm. Combination of SURF features and SVM classifier makes system robust for scale variation, rotation, translation and illumination variation as well as generalization is achieved. Dimension of features have been reduced by choosing a sub set of features. Whisker and box plot visualization utilized to understand the features data. Whisker plot visualization concluded about the range, skewness, median and outliers of feature data therefore, it makes the system capable to keep good features and back out from irrelevant features. Feature refinement reduces the computational complexity. The results evaluated narrate that the overall performance of proposed algorithm is efficient.
Keywords: Vision System, Indian Traffic Sign Classification, Speed Up Robust Feature, Whisker And Box Plot, Support Vector Machine.
Scope of the Article: Classification.