Improving the Classification Accuracy of Accurate Traffic Sign Detection and Recognition System Using HOG and LBP Features and PCA-Based Dimension Reduction-Reference-Cited by-同舟云学术

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Improving the Classification Accuracy of Accurate Traffic Sign Detection and Recognition System Using HOG and LBP Features and PCA-Based Dimension Reduction

Published:2019 Issue: Volume: Page:
ISSN:1556-5068
Container-title:SSRN Electronic Journal
language:en
Short-container-title:SSRN Journal

Author:

Soni Deepali,Chaurasiya Rahul Kumar,Agrawal Sneha

Publisher

Elsevier BV

Reference29 articles.

1. Sustainable Network Resource Management System for Virtual Private Clouds

2. Ensemble of SVM for Accurate Traffic Sign Detection and Recognition

3. Traffic sign detection and recognition based on random forests;A Ellahyani;Appl. Soft Comput. J,1995

Cited by 6 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. A Vision–Language Model-Based Traffic Sign Detection Method for High-Resolution Drone Images: A Case Study in Guyuan, China;Sensors;2024-09-06

2. A Survey on Traffic Sign Classification using Artificial Intelligence Techniques;2024 International Conference on Intelligent Systems and Computer Vision (ISCV);2024-05-08

3. Zero-Shot Traffic Sign Recognition Based on Midlevel Feature Matching;Sensors;2023-12-04

4. Recent Advances in Traffic Sign Recognition: Approaches and Datasets;Sensors;2023-05-11

5. Performance Analysis of Different Classifiers Using HOG and LBP for Traffic Sign Detection;Mining Intelligence and Knowledge Exploration;2023

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